The feature selection based bisecting K-means.

Implemented bisecting K-means in Python, with the feature selection. Gradually reduce the feature dimension when the cluster size is smaller.

Feature Selection:

The feature selection is done by applying PCA to the features and reduce the dimensionality of features gradually. The dimension is positive correlated with the clsuter size.

Pipeline:

The baselien K-Means is from SKLearn. The bisecting K-means is a top-down clustering model, it starts with all in one cluster. Each time we apply K-Means to the cluster with the largest square distance, with k = 2.

Evaluation:

The silhouette scores analysis is printed at each time K-Means divide the cluster into two sub clusters.

Usage:

Simply change the file path in main, and it will read your feature.