Farthest-Point Heuristic based Initialization Methods for K-Modes Clustering

TL;DR

This paper investigates a farthest-point heuristic for initializing k-modes clustering, demonstrating that it improves clustering accuracy over random initialization through experimental validation.

Contribution

It introduces a novel farthest-point heuristic initialization method for k-modes clustering and empirically shows its effectiveness.

Findings

Farthest-point heuristic improves clustering accuracy.

The method outperforms random initialization.

Experimental results validate the approach.

Abstract

The k-modes algorithm has become a popular technique in solving categorical data clustering problems in different application domains. However, the algorithm requires random selection of initial points for the clusters. Different initial points often lead to considerable distinct clustering results. In this paper we present an experimental study on applying a farthest-point heuristic based initialization method to k-modes clustering to improve its performance. Experiments show that new initialization method leads to better clustering accuracy than random selection initialization method for k-modes clustering.