Fast Clustering of Categorical Big Data

TL;DR

This paper introduces BK-Modes, a bisecting approach to improve initial cluster centers for K-Modes, resulting in better clustering quality and efficiency for large categorical datasets.

Contribution

The paper proposes BK-Modes, a novel bisecting method for selecting initial centers in K-Modes, enhancing clustering performance on big data.

Findings

BK-Modes outperforms existing methods in clustering quality.

BK-Modes is more efficient for large datasets.

Experimental results show improved performance in both quality and speed.

Abstract

The K-Modes algorithm, developed for clustering categorical data, is of high algorithmic simplicity but suffers from unreliable performances in clustering quality and clustering efficiency, both heavily influenced by the choice of initial cluster centers. In this paper, we investigate Bisecting K-Modes (BK-Modes), a successive bisecting process to find clusters, in examining how good the cluster centers out of the bisecting process will be when used as initial centers for the K-Modes. The BK-Modes works by splitting a dataset into multiple clusters iteratively with one cluster being chosen and bisected into two clusters in each iteration. We use the sum of distances of data to their cluster centers as the selection metric to choose a cluster to be bisected in each iteration. This iterative process stops when K clusters are produced. The centers of these K clusters are then used as the initial cluster centers for the K-Modes. Experimental studies of the BK-Modes were carried out and were compared against the K-Modes with multiple sets of initial cluster centers as well as the best of the existing methods we found so far in our survey. Experimental results indicated good performances of BK-Modes both in the clustering quality and efficiency for large datasets.