A probabilistic framework for optimizing projected clusters with categorical attributes

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• 作者：LiFei Chen
• 关键词：projective clustering ; projected cluster ; categorical data ; probabilistic framework ; kernel density estimation ; attribute weighting ; 072104 ; 鎶曞奖鑱氱被 ; 绫诲睘鏁版嵁 ; 姒傜巼鏋勬灦 ; 鏍稿瘑搴︿及璁?/li> 灞炴€у姞鏉?/li>
• 刊名：SCIENCE CHINA Information Sciences
• 出版年：2015
• 出版时间：July 2015
• 年：2015
• 卷：58
• 期：7
• 页码：1-15
• 全文大小：535 KB
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• 作者单位：LiFei Chen (1)
  
  1. School of Mathematics and Computer Science, Fujian Normal University, Fuzhou, 350117, China
  
• 刊物类别：Computer Science
• 刊物主题：Chinese Library of Science
  Information Systems and Communication Service
  
• 出版者：Science China Press, co-published with Springer
• ISSN：1869-1919

文摘

The ability to discover projected clusters in high-dimensional data is essential for many machinelearning applications. Projective clustering of categorical data is currently a challenge due to the difficultiesin learning adaptive weights for categorical attributes coordinating with clusters optimization. In this paper,a probability-based learning framework is proposed, which allows both the attribute weights and the centerbasedclusters to be optimized by kernel density estimation on categorical attributes. A novel algorithm is thenderived for projective clustering on categorical data, based on the new learning approach for the kernel bandwidthselection problem. We show that the attribute weight substantially connects to the kernel bandwidth, whilethe optimized cluster center corresponds to the normalized frequency estimator of the categorical attributes.Experimental results on synthesis and real-world data show outstanding performance of the proposed method,which significantly outperforms state-of-the-art algorithms.