Issues,Challenges and Tools of Clustering Algorithms

TL;DR

This paper reviews the common issues, challenges, and tools associated with clustering algorithms, discussing their implementation, validation, and practical considerations in data mining.

Contribution

It provides a comprehensive overview of clustering problems, available tools, and validation methods, aiding practitioners in effective clustering implementation.

Findings

Identifies key challenges faced during clustering implementation.

Discusses widely used tools and support functions for clustering.

Reviews various validation indexes for assessing clustering performance.

Abstract

Clustering is an unsupervised technique of Data Mining. It means grouping similar objects together and separating the dissimilar ones. Each object in the data set is assigned a class label in the clustering process using a distance measure. This paper has captured the problems that are faced in real when clustering algorithms are implemented .It also considers the most extensively used tools which are readily available and support functions which ease the programming. Once algorithms have been implemented, they also need to be tested for its validity. There exist several validation indexes for testing the performance and accuracy which have also been discussed here.