Distributed Data Clustering : A Comparative Analysis

Authors(3) :-V. Maria Antoniate Martin, Dr. K. David, B. Merlinsuganthi

Distributed computing plays an important role in the Data Mining process. Cluster analysis is one of the most common techniques in data mining. Clustering is a task of grouping a set of objects in such a way that objects is in the same group. Data mining is a function that assigns items in a collection to target categories or classes. There are many different techniques and algorithms are available for distributed data clustering. Cluster analysis itself is not one specific algorithm, but the general task to be solved. Many researchers have proposed clustering algorithms, which work efficiently in the distributed mining. This paper compares the performance of distributed clustering algorithms namely, Distributed k-means algorithm and partition algorithm. In this research paper we have to discuss, the comparative analysis of some of these distributed clustering.

Authors and Affiliations

V. Maria Antoniate Martin
Research Scholar, Department of Computer Science, Research and Development Centre, Bharathiar University, Coimbatore, Tamil Nadu, India
Dr. K. David
Assistant Professor, Department of Computer Science, The Rajah’s College, Pudukkottai, Tamil Nadu, India
B. Merlinsuganthi
Student, Department of Information Technology, St. Joseph’s College, Trichy, Tamil Nadu, India

Distributed cluster, Centroid, k-Means, k-Medoid, CLARA

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Publication Details

Published in : Volume 3 | Issue 3 | March-April 2018
Date of Publication : 2018-04-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 860-865
Manuscript Number : CSEIT183376
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

V. Maria Antoniate Martin, Dr. K. David, B. Merlinsuganthi, "Distributed Data Clustering : A Comparative Analysis", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 3, pp.860-865, March-April-2018.
Journal URL : http://ijsrcseit.com/CSEIT183376

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