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Refinement of K-Means and Fuzzy C-Means

by A. Banumathi, A. Pethalakshmi
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 39 - Number 17
Year of Publication: 2012
Authors: A. Banumathi, A. Pethalakshmi
10.5120/4911-7441

A. Banumathi, A. Pethalakshmi . Refinement of K-Means and Fuzzy C-Means. International Journal of Computer Applications. 39, 17 ( February 2012), 11-16. DOI=10.5120/4911-7441

@article{ 10.5120/4911-7441,
author = { A. Banumathi, A. Pethalakshmi },
title = { Refinement of K-Means and Fuzzy C-Means },
journal = { International Journal of Computer Applications },
issue_date = { February 2012 },
volume = { 39 },
number = { 17 },
month = { February },
year = { 2012 },
issn = { 0975-8887 },
pages = { 11-16 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume39/number17/4911-7441/ },
doi = { 10.5120/4911-7441 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:26:39.410591+05:30
%A A. Banumathi
%A A. Pethalakshmi
%T Refinement of K-Means and Fuzzy C-Means
%J International Journal of Computer Applications
%@ 0975-8887
%V 39
%N 17
%P 11-16
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Clustering is widely used technique in data mining application for discovering patterns in large data set. In this paper the K-Means and Fuzzy C-Means algorithm is analyzed and found that quality of the resultant cluster is based on the initial seeds where it is selected either sequentially or randomly. For real time large database it’s difficult to predict the number of cluster and initial seeds accurately. In order overcome this drawback we propose two new algorithms Unique Clustering through Affinity Measure(UCAM) and Fuzzy-UCAM clustering algorithm. Both UCAM and Fuzzy-UCAM clustering algorithms works without fixing initial seeds, number of resultant cluster to be obtained. Unique clustering is obtained with the help of affinity measures.

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Index Terms

Computer Science
Information Sciences

Keywords

Cluster K-Means UCAM Fuzzy C-Means Fuzzy-UCAM

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