One key behind the success of KNIME is its inherent modular workflow approach, which documents and stores the analysis process in the order it was conceived and implemented, while ensuring that intermediate results are always available.

Core KNIME features include:

• Scalability through sophisticated data handling (intelligent automatic caching of data in the background while maximizing throughput performance)
• High, simple extensibility via a well-defined API for plugin extensions
• Intuitive user interface
• Import/export of workflows (for exchanging with other KNIME users)
• Parallel execution on multi-core systems
• Command line version for "headless" batch executions

Available KNIME modules cover a vast range of functionality, such as:

• I/O: retrieves data from files or data bases
• Data Manipulation: pre-processes your input data with filtering, group-by, pivoting, binning, normalization, aggregation, joining, sampling, partitioning, etc.
• Views: visualize data and results through several interactive views, allowing for interactive data exploration
• Hiliting: ensures hilited data points in one view are also immediately hilited in all other views
• Mining: uses state-of-the-art data mining algorithms like clustering, rule induction, decision tree, association rules, naïve bayes, neural networks, support vector machines, etc. to better understand your data

Supported Operating Systems

• Windows - 32bit (regularly tested on XP and Vista)
• Windows - 64bit (regularly tested on Vista and verified to work under Windows 7)
• Linux - 32bit (regularly tested on RHEL4/5, OpenSUSE 10.2/10.3/11.0, amongst others)
• Linux - 64bit (regularly tested on RHEL4/5, OpenSUSE 10.2/10.3/11.0, amongst others)
• Mac OSX - 64bit Intel-based architecture with Java 1.6

List of Available Nodes (Modules)

• IO
  • Read
    • File Reader - Flexible reader for ASCII files.
    • ARFF Reader - Reads ARFF data files.
    • CSV Reader - Reads CSV files.
    • Line Reader - Read lines from a file or URL.
    • Table Reader - Reads table written by the Table Writer node.
    • PMML Reader - Reads models from a PMML compliant XML file.
    • XLS Reader - Reads a data table from a spread sheet.
    • Model Reader - Reads KNIME model port objects from a file.
  • Write
    • CSV Writer - Saves a datatable into an ASCII file.
    • ARFF Writer - Writes data into a file in ARFF format.
    • Table Writer - Writes a data table to a file using an internal format.
    • PMML Writer - Reads a model from a PMML port and writes it into a PMML v3.1 compliant file.
    • Model Writer - Writes KNIME model port objects to a file.
    • XLS Writer - Saves a datatable into a spreadsheet.
  • Other
    • Data Generator - Creates random data with clusters.
    • Create Table Structure - Creates an empty table (no rows) with a predefined structure (columns).
    • Extract System Properties - Reads system properties, including current user name and working directory.
    • Image Column Writer - Writes all in a column images to a directory.
    • Image Port Writer - Writes a image port object to a file.
    • List Files - Creates a list with the locations and URLs of the files contained in a given folder.
    • Read PNG Images - Read PNG images from a list of URLs and append them as a new column.
    • Table Creator - Allows the manual creation of a data table with any number of rows and columns.
  • Cache - Caches all input data (rows) onto disk for fast access.
• Database
  • Database Reader - Establishes and opens a database access connection to read a table from.
  • Database Connector - Creates a database connection to the specified database.
  • Database Looping - This node runs SQL queries in the connected database restricted by the possible values given by the input table.
  • Database Row Filter - The Database Row Filter allows to filter rows from database table.
  • Database Query - Modifies the input SQL query from a incoming database connection.
  • Database Column Filter - The Database Column Filter allows columns to be excluded from the input table database table.
  • Database Connection Reader - Reads the entire data from the input database connection.
  • Database Connection Writer - Writes the input database table into a new database table.
  • Database Writer - Establishes and opens a database access connection to which the entire input table is written to. be written.
• Data Manipulation
  • Column
    • Binning
      • Auto-Binner - This node allows to group numeric data in intervals - called bins.
      • Auto-Binner (Apply) - This node allows to group numeric data in intervals - called bins.
      • Numeric Binner - Group values of numeric columns categorized string type.
      • Binner (Dictionary) - Categorizes values in a column according to a dictionary table with min/max values.
      • CAIM Binner - This node implements the CAIM discretization algorithm according to Kurgan and Cios (2004). The discretization is performed with respect to a selected class column.
      • CAIM Applier - Takes a binning (discretization) model and a data table as input and bins (discretizes) the columns of the input data according to the model.
    • Convert & Replace
      • Category To Number - Maps each category of a column with nominal data to an integer.
      • Category To Number (Apply) - Maps each category of a column with nominal data to an integer.
      • Cell Replacer - Replaces cells in a column according to dictionary table (2nd input).
      • Column Rename - Enables you to rename column names or to change their types.
      • Column Rename (Regex) - Renames all columns based on regular expression search & replace pattern.
      • Domain Calculator - Determines domain information of selected columns.
      • Number To String - Converts numbers in a column to strings.
      • String To Number - Converts strings in a column to numbers.
      • Double To Int - Converts double in a column to integers.
      • String Replace (Dictionary) - Replaces the values in a column by matching entries of a dictionary file.
      • String Replacer - Replaces values in string cells if they match a certain wildcard pattern.
    • Filter
      • Column Filter - The Column Filter allows columns to be excluded from the input table.
      • Reference Column Filter - The Reference Column Filter allows columns to be filtered from the first table using the second table as reference.
      • Low Variance Filter - Filters out numeric columns, which have a low variance.
    • Split & Combine
      • Cell Splitter - Splits cells in one column of the table into separate columns based on a specified delimiter.
      • Cell Splitter By Position - Splits cells in one column of the table at fixed positions into separate columns.
      • Column Combiner - Combines the content of a set of columns and appends the concatenated string as separate column to the input table.
      • Column Merger - Merges two columns into one by choosing the cell that is non-missing.
      • Create Collection Column - Combines multiple columns into a new collection column.
      • Split Collection Column - Splits a collection column into its sub components, adding one new column for each.
      • Joiner - Joins two tables
      • Regex Split - Splits an input string (column) into multiple groups according to a regular expression.
      • Splitter - Splits the columns of the input table into two output tables.
      • Column to Grid - Breaks a selected column (or set of columns) into new columns, such that they align in a grid.
    • Transform
      • Case Converter - This node converts alphanumeric characters to lowercase or UPPERCASE.
      • Column Comparator - Compares the cell values of two columns row-wise using different comparison methods. A new column is appended with the result of the comparison.
      • Column Resorter - Resorts the order of the columns based on user input
      • Denormalizer - Denormalizes the attributes of a table according to a model.
      • Missing Value - Filters or replaces missing values in a table.
      • Normalizer - Normalizes the attributes of a table.
      • Normalizer (Apply) - Normalizes the attributes of a table according to a model.
      • One2Many - Transforms the values of one column into appended columns.
      • Many2One - Aggregates several columns into one single column.
      • SMOTE - Adds artificial data to improve the learning quality using the SMOTE algorithm
      • Set Operator - Performs a set operation on two selected table columns.
      • String Manipulation - Allows to do string manipulations like search and replace, capitalize or remove leading and trailing white spaces.
      • Subset Matcher - The node matches all subsets of the first input table with all sets of the second input table.
    • HiLite Collector - Node allows to apply annotations to sets of hilit rows within a view.
  • Row
    • Filter
      • HiLite Filter - Partitions input rows based on their current hilite status.
      • Nominal Value Row Filter - Filters rows on nominal attribute value
      • Numeric Row Splitter - Node splits the input data according to a given numeric range. The first output port contains the data that matches the criteria, the second the that does not comply with the settings.
      • Reference Row Filter - The Reference Row Filter allows rows to be filtered from the first table using the second table as reference.
      • Row Filter - Allows filtering of datarows by certain criteria, such as row ID, attribute value, and row number range.
      • Row Splitter - Allows splitting of the input table by certain criteria, such as row ID, attribute value, and row number range.
    • Transform
      • Bitvector Generator - Generates bitvectors either from a table containing numerical values, or from a string column containing the bit positions to set, hexadecimal or binary strings.
      • Concatenate - Concatenates two tables row-wise.
      • Concatenate (Optional in) - Concatenates tables row-wise, inputs are optional.
      • GroupBy - Groups the table by the selected column(s) and aggregates the remaining columns using the selected aggregation method.
      • Ungroup - Creates for each list of collection values a list of rows with the values of the collection in one column and all other columns given from the original row.
      • Partitioning - Splits table into two partitions.
      • Pivoting - Pivots and groups the input table by the selected columns for pivoting and grouping; enhanced by column aggregations.
      • Row Sampling - Extracts a sample (a bunch of rows) from the input data.
      • Equal Size Sampling - Removes rows from the input data set such that the values in a categorical column are equally distributed.
      • Shuffle - Shuffles the rows of the input tables.
      • Sorter - Sorts the rows according to user-defined criteria.
      • Unpivoting - This node rotates the selected columns from the input table to rows and duplicates at the same time the remaining input columns by appending them to each corresponding output row.
    • Other
      • Add Empty Rows - Adds a certain number of empty rows with missing values or a given constant to the input table.
      • Extract Column Header - Creates new table with a single row containing the column names.
      • Insert Column Header - Updates column names of a table according to the mapping in second dictionary table.
      • RowID - Node to replace the RowID and/or to create a column with the values of the current RowID.
      • Rule Engine - Applies user-defined business rules to the input table
  • Matrix
    • Transpose - Transposes a table by swapping rows and columns.
  • PMML
    • Denormalizer - Denormalizes the attributes of a table reversing the information in the PMML model.
    • Normalizer - Normalizes the attributes of a table.
    • Normalizer (Apply) - Normalizes the attributes of a table according to a PMML model.
    • Number To String - Converts numbers in a column to strings.
    • Numeric Binner - Group values of numeric columns categorized string type.
    • One2Many - Transforms the values of one column into appended columns.
    • String To Number - Converts strings in a column to numbers.
• Data Views
  • Property
    • Color Manager - Assigns colors to a selected nominal or numeric column.
    • Size Manager - Assigns sizes corresponding to the values of one numeric column.
    • Shape Manager - Assigns shapes to one selected nominal column.
    • Color Appender - Assigns an existing color model to a table.
    • Size Appender - Appends sizes to one selected column.
    • Shape Appender - Appends shapes to one selected column.
    • Extract Color - Extract color information (RGB) from a color model.
  • JFreeChart
    • Bar Chart (JFreeChart) - Displays a bar chart for columns with nominal values.
    • Bubble Chart (JFreeChart) - This node creates a bubble-chart visualization. It uses two variables for the position of a data point and a third one is mapped to the radius of the point.
    • GroupBy Bar Chart (JFreeChart) - Displays a bar chart for all ready grouped columns with nominal values. You can use the GroupBy-Node to gain the required columns.
    • Histogram Chart (JFreeChart) - Display a histogram chart.
    • Interval Chart (JFreeChart) - Displays a interval chart.
    • Line Chart (JFreeChart) - Display a line plot using x,y data points and connects them using a line.
    • Pie Chart (JFreeChart) - Node to display a pie chart.
    • Scatter Plot (JFreeChart) - The node visualize the input data by putting each data point to the position on the plot (X and Y value).
  • Utility
    • Image To Table - Converts a given image into a table with one cell.
    • Renderer to SVG - Creates SVG cells using a renderer
  • Box Plot - A box plot displays robust statistical parameters for numerical attributes and identifies extreme outliers.
  • Conditional Box Plot - A box plot displays robust statistical parameters for numerical attributes and identifies extreme outliers. The conditional box plot partitions the data of one column into classes and creates a box plot for each of them.
  • Histogram - Displays data in a histogram view. Hiliting is not supported.
  • Histogram (interactive) - Displays data in an interactive histogram view with hiliting support.
  • Interactive Table - Displays data in a table view.
  • Lift Chart - Creates a lift chart
  • Line Plot - Plots the numeric columns as lines.
  • Parallel Coordinates - Plots the data in Parallel Coordinates.
  • Pie chart - Displays data in a pie chart. Hiliting is not supported.
  • Pie chart (interactive) - Displays data in an interactive pie chart with hiliting support.
  • Rule Viewer - This node visualizes a set of rules that are represented as a table containing numeric support, confidence, lift values and nominal values for the consequence and antecedence.
  • Scatter Matrix - Plots a scatter matrix where each column is compared to all others.
  • Scatter Plot - Creates a scatterplot of two selected attributes.
  • Spark Line Appender - Appends a column holding spark line plots based on the selected columns.
  • Radar Plot Appender - Creates radar plots for each row, summarizing selected doubles in this row
• Statistics
  • Regression
    • Linear Regression (Learner) - Performs a multivariate linear regression.
    • Polynomial Regression (Learner) - Learner that builds a polynomial regression model from the input data
    • Regression (Predictor) - Predicts the response using a regression model.
    • Logistic Regression (Learner) - Performs a multinomial logistic regression.
    • Logistic Regression (Predictor) - Predicts the response using a general regression model.
  • Linear Correlation - Computes correlation coefficients for pairs of numeric or nominal columns.
  • Correlation Filter - Filters out correlated columns.
  • Statistics - Calculates statistic moments and counts nominal values and their occurrences across all columns.
  • Crosstab - Creates a cross-tabulation (also referred as contingency table or cross-tab).
  • Value Counter - Counts the occurrences of values in a column
• Mining
  • Bayes
    • Naive Bayes Learner - Creates a naive Bayes model from the given classified data.
    • Naive Bayes Predictor - Uses the naive Bayes model from the naive Bayes learner to predict the class membership of each row in the input data.
  • Clustering
    • Cluster Assigner - Assigns data to a set of prototypes.
    • Fuzzy c-Means - Performs fuzzy c-means clustering.
    • Hierarchical Clustering - Performs Hierarchical Clustering.
    • SOTA Learner - Clusters numerical data with SOTA.
    • SOTA Predictor - Predicts classes for rows using the input SOTA model.
    • k-Means - Creates a crisp center based clustering.
  • Rule Induction
    • Fuzzy Rules
      • Fuzzy Rule Learner - Learns a Fuzzy Rule Model on labeled numeric data.
      • Fuzzy Rule Predictor - Applies a Fuzzy Rule Model to numeric data and outputs a prediction for each test instance.
  • Neural Network
    • MLP
      • MultiLayerPerceptron Predictor - Predicts output values based on a trained MLP.
      • RProp MLP Learner - Builds and learns an MLP with resilient backpropagation.
    • PNN
      • PNN Learner (DDA) - Trains a Probabilistic Neural Network (PNN) on labeled data.
      • PNN Predictor - Applies a PNN Model to numeric data and outputs a classification.
  • Decision Tree
    • Decision Tree Learner - Decision tree induction performed in memory.
    • Decision Tree Predictor - Uses an existing decision tree to compute class labels for input vectors.
    • Decision Tree To Image - Renders a decision tree view on an image (PNG).
    • J48 (Weka) - Generates an unpruned or pruned C4.5 decision tree (WEKA).
  • Misc Classifiers
    • K Nearest Neighbor - Classifies a set of test data based on the k Nearest Neighbor algorithm using the training data.
  • Ensemble Learning
    • Utility Nodes
      • Boosting Learner Loop End - Loop end node for learning an ensemble model with boosting
      • Boosting Learner Loop Start - Loop start node for learning an ensemble model with boosting
      • Boosting Predictor Loop End - Loop end node for predicting a boosted ensemble model
      • Boosting Predictor Loop Start - Loop start node for predicting a boosted ensemble model
      • Cell To Model - Converts a single table cell into a model output.
      • Cell To PMML - Converts the PMML cell in the first Row to the PMML Port.
      • Delegating Loop End - The loop end node for performing delegating, a version of meta learning.
      • Delegating Loop Start - This is the loop start for performing delegating, a version of meta learning.
      • Model Loop End - Collects and combines all models provided during the loop iterations.
      • Model Loop Start - Provides one model from the input table at a time to the output.
      • Model to Cell - Converts a model input into a single table cell.
      • PMML Predictor - Can predict the data using the PMML object.
      • PMML To Cell - Converts the PMML Port to a table containing the PMML cell.
      • Voting Loop End - Determines the most frequent value from a collection of prediction columns.
    • Bagging - Bagging
    • Boosting Learner - Boosting Learner
    • Boosting Predictor - Boosting Predictor
    • Delegating - Delegating
  • Item Sets / Association Rules
    • Association Rule Learner - Searches for frequent itemsets with a certain minimum support in a set of transactions and optionally generates association rules with a predefined confidence value from them.
    • Association Rule Learner - Provides different algorithms to searches for frequent items in a list of item sets.
    • Bitvector Generator - Generates bitvectors either from a table containing numerical values, or from a string column containing the bit positions to set, hexadecimal or binary strings.
    • Item Set Finder - Provides different algorithms to searches for frequent items in a list of item sets.
    • Subset Matcher - The node matches all subsets of the first input table with all sets of the second input table.
  • MDS
    • MDS - Multi dimensional scaling node, mapping data of a high dimensional space onto a lower dimensional space by applying the Sammons mapping.
    • MDS Projection - Multi dimensional scaling node, mapping data of a high dimensional space onto a lower dimensional space by applying a modified Sammons mapping with respect to a given set of fixed points.
  • PCA
    • PCA - Principal component analysis
    • PCA Compute - Principal component analysis computation
    • PCA Apply - Apply principal components projection
    • PCA Inversion - Inverse the PCA transformation
  • SVM
    • LIBSVM
      • LIBSVMLearner - LIBSVM is an integrated software for support vector classification.
      • LIBSVMPredictor - Takes a trained LIBSVM to predict the values for new data.
    • SVM Learner - Trains a support vector machine.
    • SVM Predictor - This node uses a SVM model generated by the SVM learner node to predict the output for given parameters.
  • Scoring
    • Enrichment Plotter - Draws enrichment curves
    • Entropy Scorer - Scorer for clustering results given a reference clustering.
    • ROC Curve - Shows ROC curves
    • Scorer - Compares two columns by their attribute value pairs.
  • Meta
    • Cross Validation
      • X-Partitioner - Data partitioner for use in a cross-validation flow
      • X-Aggregator - Node that aggregates the result for cross validation.
    • Feature Selection
      • Backward Feature Elimination Start (1:1) - Start node for a backward feature elimination loop
      • Backward Feature Elimination Start (2:2) - Start node for a backward feature elimination loop
      • Backward Feature Elimination End - End node for a backward feature elimination loop
      • Backward Feature Elimination Filter - Applies a feature filter model built during backward feature elimination
• Chemistry
  • I/O
    • Mol2 Reader - Reads molecules from a Mol2 file
    • Mol2 Writer - Writes a Mol2 column to a Mol2 file.
    • Molfile Reader - Reads molecules from a directory with Molfiles
    • Molfile Writer - Writes molecules as Molfiles to a directory
    • SDF Reader - Reads molecules from an MDL SDF file
    • SDF Writer - Writes molecules to an MDL SDF file
    • Smiles Directory Reader - Reads molecules from a directory with Smiles files
    • Smiles Directory Writer - Writes molecules as single files to a directory
  • Mining
    • Fingerprint Bayesian (Learner) - (Variant) of Naive Bayes for fingerprint columns, i.e. bitvectors.
    • Fingerprint Bayesian (Predictor) - Predictor to the Fingerprint Bayesian (Learner) node, assigning score values to test data.
    • MoSS - searches for frequent fragments in a set of molecules.
  • Misc
    • SDF Extractor - Extracts the the various parts from SDF molecules into columns
    • SDF Inserter - Inserts properties to SDF/Mol/Ctab structures
  • Translators
    • Molecule Type Cast - Converts a String column to typed molecule column
    • OpenBabel - Converts various molecular file formats into each other
• ChemAxon / Infocom
  • Marvin
    • MarvinSketch - MarvinSketch is a chemical structures editor tool.
    • MarvinView - MarvinView is a chemical structures visualization tool.
    • MarvinSpace - MarvinSpace is a 3D molecule visualization tool.
    • MolConverter - MolConverter converts between various data types.
• Distance Matrix
  • Distance Matrix Reader - Reads triangular or full distance matrix.
  • Distance Matrix Writer - Writes column containing distance matrix to file.
  • Distance Matrix Calculate - Calculates distance matrix on input table and appends result as (typed) column.
  • k-Medoids - Performs k-Medoids algorithm.
  • Hierarchical Clustering (DistMatrix) - Performs Hierarchical Clustering on distance matrix input.
  • Hierarchical Cluster View - Shows the results of hierarchical clustering.
  • Hierarchical Cluster Assigner - Assigns clusters to rows based on an hierarchical clustering.
• Meta
  • Feature Elimination - Backward Feature Elimination
  • Iterate List of Files - Iteratively executes the contained flow on a list of files. The list of files needs to be defined by the input table, whereby each row represents one individual file location.
  • Loop x-times - Executes the contained workflow multiple times. Aggregation method and termination criteration must be set using the loop start and end node contained in the workflow.
  • Variables Loop (Data)
  • Variables Loop (Database)
  • X-Validation - Provides a skeleton of nodes necessary for cross validation
• Flow Control
  • Loop Support
    • Counting Loop Start - Node at the start of a loop
    • Chunk Loop Start - Chunking loop start, each iteration processes different chunk of rows.
    • Column List Loop Start - Iterates over a list of columns
    • Generic Loop Start - Generic loop start node with no termination criterion.
    • TableRow To Variable Loop Start - Iterates over an input data table, whereby each row defines on iteration with variable settings taken from the values in that row
    • Loop End - Node at the end of a loop
    • Variable Condition Loop End - Loop end node that check for a condition in one of the flow variables
    • Interval Loop Start - Node at the start of a loop
    • Loop End (2 ports) - Node at the end of a loop
    • Loop End (Column Append) - Node at the end of a loop, collecting the intermediate results by joining the tables on their row IDs.
  • Switches
    • IF Switch - Allows to manually control which branch the data will flow into.
    • End IF - Merges two branches which were initially created by an IF Switch Node.
    • CASE Switch - Outputs the input table to exactly one of the output ports.
    • End CASE - Merges 1-3 branches which were initially created by an IF or CASE Switch Node.
    • End (Model) CASE - Merges two or more branches with arbitrary models which were initially created by an IF or CASE Switch Node.
    • Java IF (Table) - Java IF Switch on tables
    • Empty Table Replacer - Replaces an empty table with an inactive branch.
  • Variables
    • Java Edit Variable - Edit a flow variable using java code.
    • Extract Variables (Data) - Extracts Variables from a data connection.
    • Extract Variables (Database) - Extracts Variables from a Database connection.
    • Inject Variables (Data) - Merges Variables from one connection into the data connection. The data is simply handed through.
    • Inject Variables (Database) - Merges Variables from one connection into the database connection. The database is simply handed through.
    • TableRow To Variable - Defines new flow variables based on a single row of the input table and exposes them using a variable connection.
    • Variable Based File Reader - ASCII file reader from variable locations
    • Variable To TableColumn - Appends one or more variables as new column(s) to the data table.
    • Variable To TableRow - Extracts variables and puts them into a single row table.
• Misc
  • Java Snippet
    • Java Snippet - Calculates a new column based on java code snippets.
    • Java Snippet Row Filter - Java Snippet based Row Filter
    • Java Snippet Row Splitter - Java Snippet based Row Splitter
  • Create Temp Dir - Creates a temporary directory upon execute and exposes its path as flow variable.
  • External SSH Tool - Executes an external tool on a remote machine via SSH.
  • Generic Webservice Client - Accesses document-style webservices
  • Math Formula - Evaluates mathematical formula, appending result as a new column or replacing an input column.
  • External Tool - Executes an external tool.
• KNIME Labs
  • Image Processing
    • IO
      • Picture Chooser - reads images from a directory.
    • Preprocessing
      • LowPass Filter - does a LowPass Filtering on the images in the selected column.
    • Segmentation
      • Binary Image Segmentation - segments an image based on a binary signal image.
      • Threshold - thresholds the input images locally with otsu thresholding.
      • Voronoi Segmentation - Voronoi based segmentation
    • Features
      • Histogram Node - calculates the histogram features of an image.
      • Line Node - calculates the line features of an image.
      • Texture Node - calculates the texture features of an image.
      • Zernike Converter - Transforms complex numbers into normal double values using the magnitude.
      • Zernike Node - calculates the zernike features of an image.
      • Zernike Reconstructor - Reconstructs an image based on zernike features.
    • Views
      • Picture Hilite TableView - Allows to hilite segments in an image.
      • RGB Merge - merges three gray value images into a color image.
    • Misc
      • IJ Macro - Executes an ImageJ macro
  • Modular Data Generation
    • Categorical
      • Conditional Label Assigner - Assigns the classes based on the probabilities to the rows.
      • One Rule Inserter - Inserts one specific rule to the given data set.
      • Random Item Inserter - Assigns the labels based on the probabilities to the rows.
      • Random Label Assigner - Assigns the labels based on the probabilities to the rows.
      • Random Label Assigner (Data) - Assigns the labels based on the probabilities to the rows.
    • Misc
      • Empty Table Creator - Creates an empty table: simply lines and RowKeys (no columns).
      • One Row to Many - Creates duplicates of the rows, based on an integer column.
      • Random Matcher - Assigns the information from the second table randomly to the rows of the first table
      • Stresser - Adds stress (outliers) to the values.
      • TimeDelay Loop End - The end loop node for the TimeDelayLoopStart collects the input line and transfers it to the start node
      • TimeDelay Loop Start - This looping double creates new rows, based on the values of input.
    • Numerical
      • Beta Distributed Assigner - Assigns a value based on the class column. This value is beta distributed.
      • Gamma Distributed Assigner - Assigns a value based on the class column. This value is gamma distributed.
      • Gaussian Distributed Assigner - Assigns a value based on the class column. This value is Gaussian distributed as defined in the configuration by its mean and standard deviation.
      • Random Number Assigner - Assigns a value based on the class column. This value is uniformly distributed between given min. and max.
  • Neighborgrams
    • Universe Marker - Defines universes (i.e. descriptors) for processing in Neighborgrams or Fuzzy CU Means.
    • Universe Marker (Apply) - Applies universe definition as given from Universe Marker node.
    • NG Construct&View - Generator and Viewer for Neighborgrams
    • NG Construct (beta) - Generator for Neighborgram data structure; suitable for further processing with NG Visual Clustering node.
    • NG Visual Clusterer (beta) - Interactive clusterer for neighborgrams, which were constructed with the NG Construct node.
    • NG Learner (beta) - Learns a classification model based on Neighborgrams.
    • NG Predictor (beta) - Predictor node for Neighborgram classification model.
  • Network
    • IO
      • File
        • BeeF Reader - Parses a specified BeeF file and creates a node, edge and feature table.
        • Network Reader - Reads a network from a file.
        • Network Writer - Writes the given network to a file.
      • Assign Partition - Assigns objects to the given partition of the given type.
      • Feature Inserter - Inserts features from a data table into the network.
      • Network Creator - Creates a new empty network.
      • Object Inserter - Inserts nodes and edges from a data table into the network.
    • Convert
      • Matrix
        • Edge Adjacency Matrix - Creates the edge adjacency matrix.
        • Incidence Matrix - Creates the incidence matrix.
        • Laplacian Matrix - Creates the laplacian matrix.
        • Matrix to Network - Creates a network from the matrix.
        • Node Adjacency Matrix - Creates the node adjacency matrix.
      • Table
        • Edge Table - Writes the id and label of all edges and their incident nodes of the incoming network to a data table.
        • Feature Table - Writes selected features of the incoming network to a data table.
        • Node Neighbor Extractor - Extracts all neighbors of the given node ids.
        • Node Table - Writes all nodes of the incoming network to a data table.
        • Partition Table - Creates a data table that contains the partition names per object for the selected partition types.
      • Network Feature Extractor - Creates for each network a feature vector.
      • Network To Row - Converts the given network into a data row.
      • Row To Network - Converts the network cells of a data table into a single network.
      • SubGraph Extractor - Extracts sub graphs from the given network.
    • Filter
      • Edge Degree Filter - Filters all edges by their number of nodes.
      • Edge Weight Filter - Filters all edges by their weight.
      • Feature Filter - Hides features from the input network. Can be used to convert a directed into an undirected network by filtering the is_target feature.
      • Feature List Filter - This filter either bypasses or filters the network elements (nodes/edges) which features match the values of the given data table.
      • Feature Value Filter - This filter either bypasses or filters the network elements (nodes/edges) that match the given filter value.
      • Neighbor Filter - Filters all objects pairs from the input table that are not adjacent/incident in the network.
      • Node Degree Filter - Filters all nodes by their degree e.g. number of incoming/outgoing edges.
      • Node Name Filter - Filters all nodes by their label.
      • Object ID Filter - Filters all objects e.g. edges and nodes by their id.
      • Partition Filter - This node filters all objects (nodes and/or edges) that are member of the selected partition(s).
    • Mining
      • Network Analyzer - Analyzes the network.
      • Partition Graph Creator - Creates a partition graph from the input graph.
      • Shortest Path - Finds the shortest paths between two defined objects.
    • Visualization
      • Network Viewer - Visualizes a network.
      • Viz Input Connector - Read networks from external program.
      • Viz Output Connector - Send networks to external program.
    • Network Generator - Generates nodes and edges depending on the selected algorithms and inserts them into the given network.
  • Parallel Execution
    • Parallel Chunk End - Represents the end of a parallel chunked flow.
    • Parallel Chunk End (multi port) - Represents the end of a parallel chunked flow.
    • Parallel Chunk Start - Represents the start of a parallel chunked flow.
  • Python
    • JPython Function - Executes a JPython function
    • JPython Script 1:1 - Executes a JPython script, taking 1 input table and returning 1 output table.
    • JPython Script 2:1 - Executes a JPython script, taking 2 input tables and returning 1 output table.
  • Text Processing
    • IO
      • Dml Document Parser - Parses dml formatted documents.
      • Document Grabber - Downloads and parsers documents.
      • Flat File Document Parser - Parses flat text files.
      • PDF Parser - Parses PDF files.
      • PubMed Document Parser - Parses PubMed search results documents.
      • Sdml Document Parser - Parses sdml formatted documents.
      • Word Parser - Parses Word (.doc) files.
    • Enrichment
      • Abner tagger - Assigns biomedical named entity tags to terms.
      • Dictionary tagger - Assigns specified tags values of specified tag types to terms specified in an dictionary column.
      • OpenNLP NE tagger - Assigs named entity tags, such as "PERSON" or "LOCATION".
      • Oscar tagger - Assigns chemical named entity tags to terms.
      • POS tagger - Assigns part of speech tags to terms.
      • Stanford tagger - Assigns part of speech tags to terms. Suitable for German and English texts.
    • Transformation
      • BoW creator - Bag of words creator.
      • Document Data Extractor - Extracts data from a document into data columns
      • Document vector - Creates a document vector for each document.
      • Sentence Extractor - Extracts all sentences of a document as string.
      • String to Term - Converts strings to terms.
      • Strings To Document - Converts the specified strings to documents.
      • Tags to String - Converts tags to strings.
      • Term to String - Converts terms to strings and adds a new column containing these strings.
      • Term to Structure - Converts terms to molecule structures represented as strings and adds a new column containing them.
      • Term vector - Creates a term vector for each term.
    • Preprocessing
      • Abner Filter - Filters terms with certain biomedical named entity tags.
      • Case converter - Converts terms to lower or upper case.
      • Dict Replacer - Replaces whole terms that match with dictionary keys with corresponding specified values.
      • Hyphenator - Hyphenates terms.
      • Kuhlen Stemmer - Stems terms with the Kuhle stemming algorithm.
      • Modifiable Term Filter - Filters terms which are set modifiable or unmodifiable, respectively.
      • N Chars Filter - Filters terms consisting of less than N characters.
      • Number Filter - Filters term consisting of numbers.
      • Oscar Filter - Filters terms with certain chemical named entity tags.
      • POS Filter - Filters terms with certain POS tags.
      • Porter Stemmer - Stems terms the Porter way.
      • Punctuation Erasure - Erases the punctuation characters of terms.
      • RegEx Filter - Filters all terms matching the specified regular expression.
      • Replacer - Replaces pattern in terms matching the specified regular expression with the defined replacement.
      • STTS Filter - Filters terms with certain STTS tags.
      • Snowball Stemmer - Stems terms with the Snowball stemmer.
      • Standard Named Entity Filter - Filters terms with standard named entity tags not specified in the dialog.
      • Stop word Filter - Filters terms contained in the stop word file.
      • Term Grouper - Groups terms by their text.
    • Frequencies
      • Frequency Filter - Filters terms with a certain frequency value.
      • ICF - Computes the inverse category frequency (icf) of each term according to the given set of documents, categories of documents respectively, and adds a column containing the icf value.
      • IDF - Computes the inverse document frequency (idf) of each term according to the given set of documents and adds a column containing the idf value.
      • TF - Computes the relative term frequency (tf) of each term according to each document and adds a column containing the tf value.
    • Misc
      • Category to class - Adds a class (string) column to each row, containing the category string of the document in that particular row.
      • Chi-square keyword extractor - Extracts relevant keywords from documents.
      • Document Viewer - Displays all data of the given documents, like text, authors, publication date and so on.
      • Keygraph keyword extractor - Extracts relevant keywords from documents.
      • String Matcher - The node finds for each string in the data list the most similar words of the dictionary list.
      • Tag Cloud - Creates a tag cloud
    • Meta
      • Extended NER Preprocessing
      • Frequencies
      • Simple Preprocessing
      • Vector Creation
  • Web Analytics
    • Byte Converter - This node allows the values of a column to be converted between Byte, Kilobyte, Megabyte, Gigabyte and Terabyte.
    • Country Extractor - Extracts the corresponding country for a given IP address.
    • Weblog Reader - This node reads Apache log files.
  • External Tool (Labs) - Process the input data using an external application.
  • PPilot Connector - Accesses Pipeline Pilot web services.
  • Perl Scripting - Runs a Perl script which creates a new column or replaces an existing one.
• Time Series
  • Date Field Extractor - Extracts date fields from a date/time and appends the values as integer columns.
  • Time Field Extractor - Extracts time fields such as and appends the value as integer columns.
  • Extract Time Window - Extracts all rows within the specified time window.
  • Mask Date/Time - Masks (removes) date or time fields from existing date/time.
  • Moving Average - Adds a column with moving average values.
  • Preset Date/Time - Presets date or time to timestamps lacking this information.
  • String to Date/Time - Parses date and/or time strings into date/time cells.
  • Time Difference - Appends the difference between two dates.
  • Time Generator - Generates time values
  • Time to String - Converts a timestamp column into a column holding strings.
• Quick Form
  • Date (String) Input - Outputs a date in a string flow variable with a given value.
  • Double Input - Outputs a double-precision floating point variable with a given value.
  • File Download - Provides a KNIME quick form with a downloadable file.
  • File Upload - Quick Form node that allows uploading a file and exposing that uploaded file using a flow variable.
  • Integer Input - Outputs an integer flow variable with a given value.
  • String Input - Outputs a string flow variable with a given value.
  • String Radio Buttons - Outputs a string flow variable with a given value.
  • Variable Output - Provides the value of a selected variable to a remote quick form.
• R
  • Local
    • R Learner (Local) - Allows execution of R commands in a local R installation and build a R model.
    • R Predictor (Local) - Allows to import a R model and predict given data by the use of the model.
    • R Snippet (Local) - Allows execution of R commands in a local R installation.
    • R To PMML (Local) - Converts a given R object into a corresponding PMML object.
    • R View (Local) - Enables the usage of R views using the local R installation.
  • Remote
    • R Snippet (Remote) - Allows execution of R commands on an R server. The result of these R commands is returned in the output table of this node. The final result tables' columns are named R1, R2, and so on.
    • R View (Remote) - Enables the usage of R views generated on an R server.
  • IO
    • R Model Reader - Reads an R model from a file.
    • R Model Writer - Writes an R model to a (zip) file.
• Reporting
  • Table Writer
    • Table to HTML - Generates HTML reports out of input data by using the Birt reporting engine.
    • Table to PDF - Generates PDF reports out of input data by using the Birt reporting engine.
  • Data to Report - Provides the incoming data to the KNIME Report Designer.
  • Image to Report - Provides the incoming image to the KNIME Report Designer.
• Testing
  • Block Programmatically - Uses a lock to hold the execution until a prgrammatic "release" is triggered.
  • Count Execution Programmatically - Counts the number of executions. Used in workflow manager unit tests.
  • Difference Checker - Compares the two input tables.
  • Disturber Node - Takes the input table, and creates three different output tables from it.
  • Fail in execution - Node that always fails upon execution.
  • File Difference Checker - Compares the two files.
  • Model Content Difference Checker - Compares the two input models.
  • Test Data Generator - Creates a data table with all common data types.
  • Testflow Configuration - Configures a workflow test
• Weka
  • Classification Algorithms
    • bayes
      • AODE - AODE achieves highly accurate classification by averaging over all of a small space of alternative naive-Bayes-like models.
      • BayesNet - Bayes Network learning using various search algorithms and quality measures.
      • ComplementNaiveBayes - Class for building and using a Complement class Naive Bayes classifier.
      • HNB - Contructs Hidden Naive Bayes classification model with high classification accuracy and AUC.
      • NaiveBayes - Class for a Naive Bayes classifier using estimator classes.
      • NaiveBayesMultinomial - Class for building and using a multinomial Naive Bayes classifier.
      • NaiveBayesMultinomialUpdateable - Class for building and using a multinomial Naive Bayes classifier.
      • NaiveBayesSimple - Class for building and using a simple Naive Bayes classifier.
      • NaiveBayesUpdateable - Class for a Naive Bayes classifier using estimator classes.
      • WAODE - WAODE contructs the model called Weightily Averaged One-Dependence Estimators.
    • functions
      • GaussianProcesses - Implements Gaussian Processes for regression without hyperparameter-tuning.
      • IsotonicRegression - Learns an isotonic regression model.
      • LeastMedSq - Implements a least median sqaured linear regression utilising the existing weka LinearRegression class to form predictions.
      • LinearRegression - Class for using linear regression for prediction.
      • Logistic - Class for building and using a multinomial logistic regression model with a ridge estimator.
      • MultilayerPerceptron - A Classifier that uses backpropagation to classify instances.
      • PLSClassifier - A wrapper classifier for the PLSFilter, utilizing the PLSFilter's ability to perform predictions.
      • PaceRegression - Class for building pace regression linear models and using them for prediction.
      • RBFNetwork - Class that implements a normalized Gaussian radial basisbasis function network.
      • SMO - Implements John Platt's sequential minimal optimization algorithm for training a support vector classifier.
      • SMOreg - Implements Alex Smola and Bernhard Scholkopf's sequential minimal optimization algorithm for training a support vector regression model.
      • SVMreg - SVMreg implements the support vector machine for regression.
      • SimpleLinearRegression - Learns a simple linear regression model.
      • SimpleLogistic - Classifier for building linear logistic regression models.
      • VotedPerceptron - Implementation of the voted perceptron algorithm by Freund and Schapire.
      • Winnow - Implements Winnow and Balanced Winnow algorithms by Littlestone.
    • lazy
      • IB1 - Nearest-neighbour classifier.
      • IBk - K-nearest neighbours classifier.
      • KStar - K* is an instance-based classifier, that is the class of a test instance is based upon the class of those training instances similar to it, as determined by some similarity function.
      • LBR - Lazy Bayesian Rules Classifier.
      • LWL - Locally weighted learning.
    • meta
      • nestedDichtonomies
        • ClassBalancedND - A meta classifier for handling multi-class datasets with 2-class classifiers by building a random class-balanced tree structure.
        • DataNearBalancedND - A meta classifier for handling multi-class datasets with 2-class classifiers by building a random data-balanced tree structure.
        • ND - A meta classifier for handling multi-class datasets with 2-class classifiers by building a random tree structure.
      • AdaBoostM1 - Class for boosting a nominal class classifier using the Adaboost M1 method.
      • AdditiveRegression - Meta classifier that enhances the performance of a regression base classifier.
      • AttributeSelectedClassifier - Dimensionality of training and test data is reduced by attribute selection before being passed on to a classifier.
      • Bagging - Class for bagging a classifier to reduce variance.
      • CVParameterSelection - Class for performing parameter selection by cross-validation for any classifier.
      • ClassificationViaRegression - Class for doing classification using regression methods.
      • CostSensitiveClassifier - A metaclassifier that makes its base classifier cost-sensitive.
      • Dagging - This meta classifier creates a number of disjoint, stratified folds out of the data and feeds each chunk of data to a copy of the supplied base classifier.
      • Decorate - DECORATE is a meta-learner for building diverse ensembles of classifiers by using specially constructed artificial training examples.
      • END - A meta classifier for handling multi-class datasets with 2-class classifiers by building an ensemble of nested dichotomies.
      • FilteredClassifier - Class for running an arbitrary classifier on data that has been passed through an arbitrary filter.
      • Grading - Implements Grading. The base classifiers are "graded".
      • GridSearch - Performs a grid search of parameter pairs for the a classifier (Y-axis, default is LinearRegression with the "Ridge" parameter) and the PLSFilter (X-axis, "# of Components") and chooses the best pair found for the actual predicting.
      • LogitBoost - Class for performing additive logistic regression.
      • MetaCost - This metaclassifier makes its base classifier cost-sensitive.
      • MultiBoostAB - Class for boosting a classifier using the MultiBoosting method.
      • MultiClassClassifier - A metaclassifier for handling multi-class datasets with 2-class classifiers.
      • MultiScheme - Class for selecting a classifier from among several using cross validation on the training data or the performance on the training data.
      • OrdinalClassClassifier - Meta classifier that allows standard classification algorithms to be applied to ordinal class problems.
      • RacedIncrementalLogitBoost - Classifier for incremental learning of large datasets by way of racing logit-boosted committees.
      • RandomCommittee - Class for building an ensemble of randomizable base classifiers.
      • RandomSubSpace - This method constructs a decision tree based classifier that maintains highest accuracy on training data and improves on generalization accuracy as it grows in complexity.
      • RegressionByDiscretization - A regression scheme that employs any classifier on a copy of the data that has the class attribute (equal-width) discretized.
      • Stacking - Combines several classifiers using the stacking method.
      • StackingC - Implements StackingC (more efficient version of stacking).
      • ThresholdSelector - A metaclassifier that selecting a mid-point threshold on the probability output by a Classifier.
      • Vote - Class for combining classifiers. Different combinations of probability estimates for classification are available.
    • misc
      • FLR - Fuzzy Lattice Reasoning Classifier (FLR) v5.0
      • HyperPipes - Class implementing a HyperPipe classifier.
      • MinMaxExtension - This class is an implementation of the minimal and maximal extension.
      • OLM - This class is an implementation of the Ordinal Learning Method.
      • OSDL - This class is an implementation of the Ordinal Stochastic Dominance Learner.
      • VFI - Classification by voting feature intervals.
    • trees
      • ADTree - Class for generating an alternating decision tree.
      • BFTree - Class for building a best-first decision tree classifier.
      • DecisionStump - Class for building and using a decision stump.
      • Id3 - Class for constructing an unpruned decision tree based on the ID3 algorithm.
      • J48 - Class for generating an alternating decision tree.
      • LMT - Classifier for building 'logistic model trees', which are classification trees with logistic regression functions at the leaves.
      • M5P - M5Base. Implements base routines for generating M5 Model trees and rules.
      • NBTree - Class for generating a decision tree with naive Bayes classifiers at the leaves.
      • REPTree - Fast decision tree learner.
      • RandomForest - Class for constructing a forest of random trees.
      • RandomTree - Class for constructing a tree that considers K randomly chosen attributes at each node.
      • SimpleCART - Class implementing minimal cost-complexity pruning.
      • UserClassifier - Interactively classify through visual means.
    • rules
      • ConjunctiveRule - This class implements a single conjunctive rule learner that can predict for numeric and nominal class labels.
      • DecisionTable - Class for building and using a simple decision table majority classifier.
      • JRip - This class implements a propositional rule learner, Repeated Incremental Pruning to Produce Error Reduction (RIPPER).
      • M5Rules - Generates a decision list for regression problems using separate-and-conquer.
      • NNge - Nearest-neighbor-like algorithm using non-nested generalized exemplars (which are hyperrectangles that can be viewed as if-then rules).
      • OneR - Class for building and using a 1R classifier; in other words, uses the minimum-error attribute for prediction, discretizing numeric attributes.
      • PART - Class for generating a PART decision list.
      • Prism - Class for building and using a PRISM rule set for classification.
      • Ridor - The implementation of a RIpple-DOwn Rule learner.
      • ZeroR - Class for building and using a 0-R classifier.
  • Cluster Algorithms
    • DBScan - Martin Ester, Hans-Peter Kriegel, Joerg Sander, Xiaowei Xu: A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise.
    • DensityBasedCluster - Class for wrapping a Clusterer to make it return a distribution and density.
    • EM - Simple EM (expectation maximisation) class.
    • FarthestFirst - Cluster data using the FarthestFirst algorithm.
    • FilteredCluster - Class for running an arbitrary clusterer on data that has been passed through an arbitrary filter.
    • Optics - Mihael Ankerst, Markus M. Breunig, Hans-Peter Kriegel, Joerg Sander: OPTICS: Ordering Points To Identify the Clustering Structure.
    • SimpleKMeans - Cluster data using the k means algorithm.
    • XMeans - Cluster data using the X-means algorithm.
  • Association Rules
    • Apriori - Class implementing an Apriori-type algorithm.
    • FilteredAssociator - Class for running an arbitrary associator on data that has been passed through an arbitrary filter.
    • GeneralizedSequentialPatterns - Class implementing a GSP algorithm for discovering sequential patterns in a sequential data set.
    • PredictiveApriori - Class implementing the predictive apriori algorithm to mine association rules.
    • Tertius - Finds rules according to confirmation measure (Tertius-type algorithm).
  • Predictors
    • Weka Cluster Assigner - The Weka Cluster Assigner takes a cluster model generated in a weka node and assigns the data at the inport to the corresponding clusters.
    • Weka Predictor - The Weka Predictor takes a model generated in a weka node and classifies the test data at the inport.
  • IO
    • Weka SerializedClassifier Write - Takes a trained weka model and writes the weka classifier to a file.
    • Weka SerializedClassifier Read - A wrapper around a serialized classifier model.
    • Weka Classifier Writer - Writes a weka classification model to a (zip) file.
    • Weka Classifier Reader - Reads a weka classification model from a (zip) file.
    • Weka Clustering Writer - Writes a weka clustering model to a (zip) file.
    • Weka Clustering Reader - Reads a weka clustering model from a (zip) file.
• XML
  • XML Reader - Reads a XML File.
  • XML Writer - Writes XML Documents in a directory.
  • String To XML - Converts string cells in a column to XML.
  • XPath - Performs a XPath query on a XML column.
  • XSLT - Applies XSLT stylesheets on the cells of an XML column.
  • Column To XML - Create an XML column from input data.
  • XML Column Combiner - Merges XML columns in a single column.
  • XML Row Combiner - Concatenates the cells in a XML column.
  • XML Combine and Write - Writes XML cells in a file.