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A Comparative Study of Various Clustering Algorithms in Data Mining?

Journal: International Journal of Computer Science and Mobile Computing - IJCSMC (Vol.3, No. 11)

Publication Date:

Authors : ; ;

Page : 422-428

Keywords : Clustering algorithms; Partitioning methods; Hierarchical methods and Density based methods;

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Data mining is the process of extracting Knowledge from data. Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group are more similar to each other than to those in other groups. Clustering is one of the complicated tasks in data mining. It plays a vital role in a broad range of applications such as marketing, surveillance, fraud detection, Image processing, Document classification and scientific discovery. Lot of issues related with cluster analysis such as a high dimension of the dataset, arbitrary shapes of clusters, scalability, input parameter, complexity and noisy data are still under research. A variety of algorithms have been emerged for clustering to address these issues which causes perplexity in choosing the right algorithm for research applications. This paper deals with classification of some of the well known clustering algorithms and also their comparison based on key issues, advantages and disadvantages, which provide guidance for the selection of clustering algorithm for a specific application.

Last modified: 2014-11-22 23:21:48