Performance Comparison of Various Robust Data Clustering Algorithms

Abstract

Clustering is the process of grouping of data, where the grouping is established by finding similarities between data based on their characteristics. Such groups are termed as Clusters. A comparative study of clustering algorithms across two different data items is performed here. The performance of the various clustering algorithms is compared based on the time taken to form the estimated clusters. The experimental results of various clustering algorithms to form clusters are depicted as a graph. Thus it can be concluded as the time taken to form the clusters increases as the number of cluster increases. The farthest first clustering algorithm takes very few seconds to cluster the data items whereas the simple KMeans takes the longest time to perform clustering.