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http://dx.doi.org/10.3745/KIPSTD.2003.10D.3.417

An Effective Algorithm for Subdimensional Clustering of High Dimensional Data  

Park, Jong-Soo (성신여자대학교 컴퓨터정보학부)
Kim, Do-Hyung (성신여자대학교 컴퓨터정보학부)
Publication Information
The KIPS Transactions:PartD / v.10D, no.3, 2003 , pp. 417-426 More about this Journal
Abstract
The problem of finding clusters in high dimensional data is well known in the field of data mining for its importance, because cluster analysis has been widely used in numerous applications, including pattern recognition, data analysis, and market analysis. Recently, a new framework, projected clustering, to solve the problem was suggested, which first select subdimensions of each candidate cluster and then each input point is assigned to the nearest cluster according to a distance function based on the chosen subdimensions of the clusters. We propose a new algorithm for subdimensional clustering of high dimensional data, each of the three major steps of which partitions the input points into several candidate clutters with proper numbers of points, filters the clusters that can not be useful in the next steps, and then merges the remaining clusters into the predefined number of clusters using a closeness function, respectively. The result of extensive experiments shows that the proposed algorithm exhibits better performance than the other existent clustering algorithms.
Keywords
Data Mining; Clustering; High Dimensional Data; Algorithm;
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Times Cited By KSCI : 2  (Citation Analysis)
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