Kernel-Induced Bubble Agglomeration Algorithm for unsupervised classification: An improved clustering methodology without prior information

1st Regional Conference on Applied and Engineering Mathematics (RCAEM-I) 2010 organized by Universiti Malaysia Perlis (UniMAP) and co-organized by Universiti Sains Malaysia (USM) & Universiti Kebangsaan Malaysia (UKM), 2nd - 3rd June 2010 at Eastern & Oriental Hotel, Penang.

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Main Author:	Lim, Eng Aik
Other Authors:	ealim@unimap.edu.my
Format:	Working Paper
Language:	English
Published:	Universiti Malaysia Perlis (UniMAP) 2010
Subjects:	Kernel function Bubble Agglomeration Euclidean distance Data classification Similarity measure Regional Conference on Applied and Engineering Mathematics (RCAEM)
Online Access:	http://dspace.unimap.edu.my/xmlui/handle/123456789/10330
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Institution:	Universiti Malaysia Perlis
Language:	English

id	my.unimap-10330
record_format	dspace
spelling	my.unimap-103302010-11-28T02:26:23Z Kernel-Induced Bubble Agglomeration Algorithm for unsupervised classification: An improved clustering methodology without prior information Lim, Eng Aik ealim@unimap.edu.my Kernel function Bubble Agglomeration Euclidean distance Data classification Similarity measure Regional Conference on Applied and Engineering Mathematics (RCAEM) 1st Regional Conference on Applied and Engineering Mathematics (RCAEM-I) 2010 organized by Universiti Malaysia Perlis (UniMAP) and co-organized by Universiti Sains Malaysia (USM) & Universiti Kebangsaan Malaysia (UKM), 2nd - 3rd June 2010 at Eastern & Oriental Hotel, Penang. This paper introduces an improved unsupervised clustering algorithm, named Kernel-Induced Bubble Agglomeration. In this paper, the conventional Bubble Agglomeration algorithm is extended by calculating the Euclidean distance of each data point based on a kernel-induced distance instead of the conventional sum-of-squares distance. The kernel function is a generalization of the distance metric that measures the distance between two data points as the data points are mapped into a high dimensional space. By using a kernel function, data that are not easily separable in the original space can be clustered into homogeneous groups in the implicitly transformed high dimensional feature space. Application of the conventional Bubble Agglomeration algorithm and the Kernel-induced Bubble Agglomeration algorithm to well-known data sets showed the superiority of the proposed approach. 2010-11-28T02:26:23Z 2010-11-28T02:26:23Z 2010-06-02 Working Paper Vol.4(8), p.395-400 http://hdl.handle.net/123456789/10330 en Proceedings of the 1st Regional Conference on Applied and Engineering Mathematics (RCAEM-I) 2010 Universiti Malaysia Perlis (UniMAP) Institut Matematik Kejuruteraan
institution	Universiti Malaysia Perlis
building	UniMAP Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaysia Perlis
content_source	UniMAP Library Digital Repository
url_provider	http://dspace.unimap.edu.my/
language	English
topic	Kernel function Bubble Agglomeration Euclidean distance Data classification Similarity measure Regional Conference on Applied and Engineering Mathematics (RCAEM)
spellingShingle	Kernel function Bubble Agglomeration Euclidean distance Data classification Similarity measure Regional Conference on Applied and Engineering Mathematics (RCAEM) Lim, Eng Aik Kernel-Induced Bubble Agglomeration Algorithm for unsupervised classification: An improved clustering methodology without prior information
description	1st Regional Conference on Applied and Engineering Mathematics (RCAEM-I) 2010 organized by Universiti Malaysia Perlis (UniMAP) and co-organized by Universiti Sains Malaysia (USM) & Universiti Kebangsaan Malaysia (UKM), 2nd - 3rd June 2010 at Eastern & Oriental Hotel, Penang.
author2	ealim@unimap.edu.my
author_facet	ealim@unimap.edu.my Lim, Eng Aik
format	Working Paper
author	Lim, Eng Aik
author_sort	Lim, Eng Aik
title	Kernel-Induced Bubble Agglomeration Algorithm for unsupervised classification: An improved clustering methodology without prior information
title_short	Kernel-Induced Bubble Agglomeration Algorithm for unsupervised classification: An improved clustering methodology without prior information
title_full	Kernel-Induced Bubble Agglomeration Algorithm for unsupervised classification: An improved clustering methodology without prior information
title_fullStr	Kernel-Induced Bubble Agglomeration Algorithm for unsupervised classification: An improved clustering methodology without prior information
title_full_unstemmed	Kernel-Induced Bubble Agglomeration Algorithm for unsupervised classification: An improved clustering methodology without prior information
title_sort	kernel-induced bubble agglomeration algorithm for unsupervised classification: an improved clustering methodology without prior information
publisher	Universiti Malaysia Perlis (UniMAP)
publishDate	2010
url	http://dspace.unimap.edu.my/xmlui/handle/123456789/10330
_version_	1643789812888502272

Kernel-Induced Bubble Agglomeration Algorithm for unsupervised classification: An improved clustering methodology without prior information

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