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Comparative study of several Clustering Algorithms

Author(s): Prof. Neha Soni, Dr. Amit Ganatra

Journal: International Journal of Advanced Computer Research
ISSN 2249-7277

Volume: 2;
Issue: 6;
Start page: 37;
Date: 2012;
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Keywords: Clustering algorithms | partitioning methods | hierarchical methods | and density based and grid based methods

Cluster Analysis is a process of grouping theobjects, where objects can be physical like a studentor can be an abstract such as behaviour of acustomer or handwriting of a person. The clusteranalysis is as old as a human life and has its rootsin many fields such as statistics, machine learning,biology, artificial intelligence. It is an unsupervisedlearning and faces many challenges such as a highdimension of the dataset, arbitrary shapes ofclusters, scalability, input parameter, domainknowledge and noisy data. Large number ofclustering algorithms had been proposed till date toaddress these challenges. There do not exist a singlealgorithm which can adequately handle all sorts ofrequirement. This makes a great challenge for theuser to do selection among the available algorithmfor the specific task. The purpose of this paper is toprovide a detailed analytical comparison of some ofthe very well known clustering algorithms, whichprovides guidance for the selection of clusteringalgorithm for a specific application.
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