Canonical PSO Based k-Means Clustering Approach for Real Datasets

TL;DR

This paper introduces a Canonical PSO based K-means clustering algorithm, evaluates various clustering indices, and compares its performance on real datasets to determine the most effective clustering method.

Contribution

The paper proposes a novel Canonical PSO based K-means algorithm and analyzes its effectiveness using multiple validity indices on real datasets.

Findings

Canonical PSO improves clustering performance over traditional methods

Different indices influence the evaluation of cluster quality

The proposed method outperforms other clustering algorithms in certain datasets

Abstract

"Clustering" the significance and application of this technique is spread over various fields. Clustering is an unsupervised process in data mining, that is why the proper evaluation of the results and measuring the compactness and separability of the clusters are important issues.The procedure of evaluating the results of a clustering algorithm is known as cluster validity measure. Different types of indexes are used to solve different types of problems and indices selection depends on the kind of available data.This paper first proposes Canonical PSO based K-means clustering algorithm and also analyses some important clustering indices (intercluster, intracluster) and then evaluates the effects of those indices on real-time air pollution database,wholesale customer, wine, and vehicle datasets using typical K-means, Canonical PSO based K-means, simple PSO based K-means,DBSCAN, and Hierarchical clustering algorithms.This paper also describes the nature of the clusters and finally compares the performances of these clustering algorithms according to the validity assessment. It also defines which algorithm will be more desirable among all these algorithms to make proper compact clusters on this particular real life datasets. It actually deals with the behaviour of these clustering algorithms with respect to validation indexes and represents their results of evaluation in terms of mathematical and graphical forms.