A Comparative study Between Fuzzy Clustering Algorithm and Hard Clustering Algorithm

TL;DR

This paper compares fuzzy clustering and hard clustering algorithms, specifically Fuzzy C-means and K-means, to evaluate their effectiveness in unsupervised data mining tasks.

Contribution

It provides a comparative analysis of fuzzy and hard clustering techniques, highlighting their differences and performance in data clustering.

Findings

Fuzzy C-means offers more flexible clustering boundaries.

K-means is faster but less adaptable to ambiguous data.

The study identifies scenarios where each algorithm performs best.

Abstract

Data clustering is an important area of data mining. This is an unsupervised study where data of similar types are put into one cluster while data of another types are put into different cluster. Fuzzy C means is a very important clustering technique based on fuzzy logic. Also we have some hard clustering techniques available like K-means among the popular ones. In this paper a comparative study is done between Fuzzy clustering algorithm and hard clustering algorithm