Instance Selection and Prototype Based Rules Example 5

Example of Instance optimization - using clustering for training kNN classifier (Requires Instance Selection and Prototype Based Rules) :

Here we use FCM (Fuzzy c-means) clustering to initialize kNN classifier. Moreover centers of clusters are determined independent for each class. "Class iterator" operator iterates over each class label, and embedded clustering algorithm cluster the examples for each class independent. The Prototype (Pro) output of "Class iterator" delivers the concatenation of cluster centers as a new ExampleSet. In this case "Class assigner" is required inside the "Class iterator" to determine the labels of cluster centers.