English  |  正體中文  |  简体中文  |  Items with full text/Total items : 26988/38789
Visitors : 2350779      Online Users : 30
RC Version 4.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Adv. Search

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

Files in This Item:

File Description SizeFormat

All items in NTOUR are protected by copyright, with all rights reserved.


著作權政策宣告: 本網站之內容為國立臺灣海洋大學所收錄之機構典藏,無償提供學術研究與公眾教育等公益性使用,請合理使用本網站之內容,以尊重著作權人之權益。
網站維護: 海大圖資處 圖書系統組
DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback