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期刊论文

CLINCH: Clustering Incomplete High-Dimensional Data for Data Mining Application

汪卫Zunping Cheng Ding Zhou Chen Wang Jiankui Guo Wei Wang Baokang Ding and Baile Shi

APWeb 2005, LNCS 3399, pp. 88-99, 2005.,-0001,():

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摘要/描述

Clustering is a common technique in data mining to discover hidden patterns from massive datasets. With the development of privacy-maintaining data mining application, clustering incomplete highdimensional data has becoming more and more useful. Motivated by these limits, we develop a novel algorithm CLINCH, which could produce fine clusters on incomplete high-dimensional data space. To handle missing attributes, CLINCH employs a prediction method that can be more precise than traditional techniques. On the other hand, we also introduce an efficient way in which dimensions are processed one by one to attack the"curse of dimensionality". Experiments show that our algorithm not only outperforms many existing high-dimensional clustering algorithms in scalability and efficiency, but also produces precise results.

版权说明:以下全部内容由汪卫上传于   2010年11月29日 09时44分21秒,版权归本人所有。

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