Once installed correctly, you will find the Kettle Knowledge Flow step in the "Transform" folder in the Spoon user interface.
4 Using the Knowledge Flow Plugin
As a simple example, We will use the Knowledge Flow step to create and export a predictive model for the "pendigits.csv"data set (docs/data/pendigits.csv). This data set is also used in the "Using the Weka Scoring Plugin"documentation.
First construct a simple Kettle transformation that links a CSV input step to the Knowledge Flow step. Next configure the input step to load the "pendigits.csv" file. Make sure that the Delimiter text box contains a "," and then click "Get Fields" to make the CSV input step analyze a few lines of the file and determine the types of the fields.
All the fields in the "pendigits.csv" file are integers. However, the problem is a discrete classification task and Weka will need the "class" field to be declared as a nominal attribute. In the CSV input step's configuration dialog, change the type of the "class" field from "Integer" to "String."