K-Histograms: An Efficient Clustering Algorithm for Categorical Dataset

TL;DR

This paper introduces k-histogram, an efficient clustering algorithm for categorical data that extends k-means by using histograms, demonstrating improved results over existing methods like k-modes.

Contribution

The paper presents a novel histogram-based extension of k-means for categorical data clustering, enhancing efficiency and accuracy.

Findings

k-histogram outperforms k-modes in clustering quality

The algorithm dynamically updates histograms during clustering

Experimental results on real datasets validate effectiveness

Abstract

Clustering categorical data is an integral part of data mining and has attracted much attention recently. In this paper, we present k-histogram, a new efficient algorithm for clustering categorical data. The k-histogram algorithm extends the k-means algorithm to categorical domain by replacing the means of clusters with histograms, and dynamically updates histograms in the clustering process. Experimental results on real datasets show that k-histogram algorithm can produce better clustering results than k-modes algorithm, the one related with our work most closely.