標題: Variance enhanced K-medoid clustering
作者: Lai, Por-Shen
Fu, Hsin-Chia
資訊工程學系
Department of Computer Science
關鍵字: Clustering;K-medoid;Data variance;Polygon model;Image similarity
公開日期: 1-一月-2011
摘要: This paper proposes new variance enhanced clustering methods to improve the popular K-medoid algorithm by adapting variance information in data clustering. Since measuring similarity between data objects is simpler than mapping data objects to data points in feature space, these pairwise similarity based clustering algorithms can greatly reduce the difficulty in developing clustering based pattern recognition applications. A web-based image clustering system has been developed to demonstrate and show the clustering power and significance of the proposed methods. Synthetic numerical data and real-world image collection are applied to evaluate the performance of the proposed methods on the prototype system. As shown as the web-demonstration, the proposed method, variance enhanced K-medoid model, groups similar images in clusters with various variances according to the distribution of image similarity values. (C) 2010 Elsevier Ltd. All rights reserved.
URI: http://dx.doi.org/10.1016/j.eswa.2010.07.030
http://hdl.handle.net/11536/26152
ISSN: 0957-4174
DOI: 10.1016/j.eswa.2010.07.030
期刊: EXPERT SYSTEMS WITH APPLICATIONS
Volume: 38
Issue: 1
起始頁: 764
結束頁: 775
顯示於類別:期刊論文


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