Performance Analysis of AIM-K-means & K-means in Quality Cluster Generation

TL;DR

This paper compares the performance of the traditional K-means clustering algorithm with the AIM-K-means extension, focusing on initial mean selection issues and their impact on clustering quality in large datasets.

Contribution

It provides an empirical analysis of AIM-K-means versus K-means, highlighting the effectiveness of automatic initial mean selection in improving clustering results.

Findings

AIM-K-means improves clustering accuracy over standard K-means.

Automatic initialization reduces the likelihood of poor local optima.

Performance varies with dataset characteristics.

Abstract

Among all the partition based clustering algorithms K-means is the most popular and well known method. It generally shows impressive results even in considerably large data sets. The computational complexity of K-means does not suffer from the size of the data set. The main disadvantage faced in performing this clustering is that the selection of initial means. If the user does not have adequate knowledge about the data set, it may lead to erroneous results. The algorithm Automatic Initialization of Means (AIM), which is an extension to K-means, has been proposed to overcome the problem of initial mean generation. In this paper an attempt has been made to compare the performance of the algorithms through implementation