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A Novel Kernel Clustering Algorithm

by Wesam M. Ashour
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 181 - Number 29
Year of Publication: 2018
Authors: Wesam M. Ashour
10.5120/ijca2018918148

Wesam M. Ashour . A Novel Kernel Clustering Algorithm. International Journal of Computer Applications. 181, 29 ( Nov 2018), 32-36. DOI=10.5120/ijca2018918148

@article{ 10.5120/ijca2018918148,
author = { Wesam M. Ashour },
title = { A Novel Kernel Clustering Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { Nov 2018 },
volume = { 181 },
number = { 29 },
month = { Nov },
year = { 2018 },
issn = { 0975-8887 },
pages = { 32-36 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume181/number29/30127-2018918148/ },
doi = { 10.5120/ijca2018918148 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:07:41.542225+05:30
%A Wesam M. Ashour
%T A Novel Kernel Clustering Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 181
%N 29
%P 32-36
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

K-means algorithm is one of the most famous clustering algorithms in data mining due to its simplicity. Kernel K-means is an extension of K-means to cluster nonlinear separable data. However, it still has some limitations like sensitivity and convergence to the local optima. In this paper, we show how to implement a new novel kernel-clustering algorithm that is robust and converges to the global solution. We show using artificial and real data sets that the proposed kernel algorithm performs better than the standard kernel K-means algorithm.

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Index Terms

Computer Science
Information Sciences

Keywords

K-means Kernel K-means Clustering global optima.

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