A semi-supervised clustering algorithm based on rough reduction

Authors:
Liandong Lin;Wei Qu;Xiang Yu
Affiliations:
Key Laboratory of Electronics Engineering, College of Heilongjiang, Heilongjiang University, Harbin, Heilongjiang Province, China;Key Laboratory of Electronics Engineering, College of Heilongjiang, Heilongjiang University, Harbin, Heilongjiang Province, China;Department of Computer Science and Technology, University of Harbin Engineering, Harbin, Heilongjiang Province, China
Venue:
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Year:
2009

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Abstract

Clustering analysis is an important issue in data mining fields. Clustering in high dimensional space is especially difficult for a series of problems, such as the sparseness of spatial distribution of data, too much noise data points. Based on the analysis of current clustering algorithms can not get satisfying clustering results of high dimensional data. The theory of rough set and the idea of semi-supervised are introduced. And a semi-supervised grid clustering algorithm RSGrid based on the reduction of rough set theory is proposed. The theoretical analysis and experimental results indicate the algorithm can solve the problem of clustering in high dimensional space efficiently.