Comparison of Clustering Algorithm

Authors(2) :-R. Indhu, R. Porkodi

Clustering is a technique used in data mining that groups similar objects into one cluster, while dissimilar objects are grouped into different clusters. Distributed data mining allows for access to volumes of data that are housed at several different company sites or at various organizations. Extremely complicated algorithms are formed to recover the essential data anyway of where it is stored so that it can be useful to a particular data model that will distribute the accurate knowledge and information. The objective of this paper is to perform a comparative analysis of four clustering algorithms namely K-means algorithm, Hierarchical algorithm and Density based algorithm and Expectation maximization algorithm. These algorithms are compared in terms of efficiency and accuracy and observed that K-means produces better results as compared to other algorithms.

Authors and Affiliations

R. Indhu
PG Scholar, Department of Computer Science, Bharathiar University, Coimbator, Tamilnadu, India
R. Porkodi
Assistant Professor, Department of Computer Science, Bharathiar University, Coimbatore, Tamilnadu, India

Clustering, K-means algorithm, Hierarchical algorithm, Expectation and maximization algorithm and Density based algorithm.

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Publication Details

Published in : Volume 3 | Issue 1 | January-February 2018
Date of Publication : 2018-02-28
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 218-223
Manuscript Number : CSEIT183137
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

R. Indhu, R. Porkodi, "Comparison of Clustering Algorithm", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 1, pp.218-223, January-February-2018.
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