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Title: Kernel-Induced Bubble Agglomeration Algorithm for unsupervised classification: An improved clustering methodology without prior information
Authors: Lim, Eng Aik
???metadata.dc.contributor.url???: ealim@unimap.edu.my
Keywords: Kernel function;Bubble Agglomeration;Euclidean distance;Data classification;Similarity measure;Regional Conference on Applied and Engineering Mathematics (RCAEM)
Issue Date: 2-Jun-2010
Publisher: Universiti Malaysia Perlis (UniMAP)
???metadata.dc.publisher.department???: Institut Matematik Kejuruteraan
Citation: Vol.4(8), p.395-400
Series/Report no.: Proceedings of the 1st Regional Conference on Applied and Engineering Mathematics (RCAEM-I) 2010
Abstract: 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.
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.
URI: http://hdl.handle.net/123456789/10330
Appears in Collections:Conference Papers

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