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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/28451

Title: Clustering via Dimension Extension and Pseudo-inverse Transformation
Authors: Yu-Chen Chen;Yu-Siang Jheng;Hong-Jie Shih;Jung-Hua Wang
Contributors: 國立臺灣海洋大學:電機工程學系
Keywords: Clustering;pseudo-inverse transformation;dimension extension;centroids;principal component analysis
Date: 2008
Issue Date: 2011-10-21T02:37:37Z
Publisher: 2008 National Symposium on System Science and Engineering (NSSSE’08)
Abstract: Abstract:Partitional clustering has a major drawback in that once some data have been divided into wrong clusters, they cannot be easily adjusted into the correct one, namely the initialization problem that plagues the k-means algorithm.
This paper presents a novel approach which incorporates Dimension Extension and Pseudo-Inverse Transformation (DEPIT) to realize data clustering. Unlike k-means algorithm, DEPIT needs not pre-specify the number of clusters k, centroids locations are updated and redundant centroids eliminated automatically during iterative training process. The essence of DEPIT is that clustering is performed by pseudo-inverse transforming the input data such that each data point is represented by a linear combination of bases with extended dimension, with each basis corresponding to a centroid and its coefficient representing the closeness between the data point and the basis.
Issue of clustering validation is also addressed in this paper. First, Principal Component Analysis is applied to detect if there exists a dominated dimension, if so, the original input data will be rotated by a certain angle w.r.t. a defined center of mass, and the resulting data undergo another run of iterative training process. After plural runs of rotation and iterative process, the labeled results from various runs are compared, a data point labeled to a centroid more times than others will be labeled to the class indexed by that wining centroid.
URI: http://ntour.ntou.edu.tw/handle/987654321/28451
Appears in Collections:[電機工程學系] 期刊論文

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