DETECTION OF LINE-SYMMETRY CLUSTERS

NCUIR > College of Electrical Engineering & Computer Science > Department of Computer Science and information Engineering > journal & Dissertation > Item 987654321/51626

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/51626

Title:	DETECTION OF LINE-SYMMETRY CLUSTERS
Authors:	Hsieh,YZ;Su,MC;Chou,CH;Wang,PC
Contributors:	資訊工程學系
Keywords:	NEURAL-NETWORKS;VALIDITY MEASURE;DATA PROJECTION;DISTANCE;IMAGES;SEPARATION;TRANSFORM;ALGORITHM;FEATURES
Date:	2011
Issue Date:	2012-03-27 18:57:51 (UTC+8)
Publisher:	國立中央大學
Abstract:	Many real-world and man-made objects are symmetry. Therefore, it is reasonable to assume that some kinds of symmetry may exist in data clusters. The most common type of symmetry is line symmetry. In this paper, we propose a line symmetry distance measure. Based on the proposed line symmetry distance, a modified version of the K-means algorithm can be used to partition data into clusters with different geometrical shapes. Several data sets are used to test the performance of the proposed modified version of the K-means algorithm incorporated with the line symmetry distance.
Relation:	INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL
Appears in Collections:	[Department of Computer Science and information Engineering] journal & Dissertation

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