The goal of the workflow is to demonstrate the creation and working with collection cells. It is divided into three parts.
The last section "Working with collections" demonstrates a sub set of the nodes that support collections such as the Item Set Finder (Borgelt) node which searches in a collection column for frequently co-occuring elements.
The goal of the workflow is to demonstrate the creation and working with collection cells. It is divided into three parts.
The "Collection types" section describes the two different collection types that are supported by KNIME namely
- Lists: list all elements including duplicates in the order they were added to the collection.
- Sets: list the elements in an arbitrary order but contain each element only once.
The goal of the workflow is to demonstrate the creation and working with collection cells. It is divided into three parts.
The "Collection creation and conversion" part shows how collection columns can be generated by combining rows e.g. by grouping them in the GroupBy node or columns e.g. by combining them in the Column Aggregator node.
This workflow shows how to use the rank correlation node to calculate Spearman's rho for the attributes of the Iris dataset.
Create a pivot table with one or more group columns and one or more pivot columns. Apply basic aggregation methods like sum and count, statistical aggregation methods, and aggregation methods available for columns of type Date&Time. Apply multiple aggregation methods to one or more aggregation columns.
On adult.csv data set: on each one of the 4 groups defined by sex and income values, calculate total number of rows and average age and write results to a CSV file; on each one of the 4 groups defined by sex and income values, calculate the average of all numerical columns; on full input table count: a. rows with missing values in column occupation; b. all rows in column occupation; c. rows with no missing value in column occupation; d. all rows in another column (i.e. marital-status). Notice that this number should be the same as the number in 2.
This workflow shows the many aggregation options that the GroupBy node offers.
We start from customer data, group on Gender or more features, and run a few different aggregation methods on a few different features. Here we demonstrate grouping on multiple features, pattern based grouping and aggregation without grouping for calculating statistics.
This workflow shows how the rank node in combination with a row filter can be used to determine the rows whose ranking column attribute is in the top 5 values for this column.
This workflow shows the many aggregation options that the GroupBy node offers.
We start from customer data, group on Gender or more features, and run a few different aggregation methods on a few different features.