A ShrinkingClustering Method for High Dimensional Data Using Flexible Size Grid
ZHANG JianYe1, PAN Quan1, LIANG JianHai2
1. School of Automation, Northwestern Polytechnical University, Xi’an 710072 2.Institute of Engineering, Air Force Engineering University, Xi’an 710038
Abstract A shrinkingclustering method using flexible size grid is proposed to solve the clustering problem of high dimensional data in data mining. The data bins are arranged according to their density span, and the data points are moved along the direction of the density gradient. Thus the condensed and widelyseparated clusters are generated. Then the connected components of dense cells are detected using a sequence of grids with flexible size. Finally, the best clustering result is obtained when the borderline does not change again. The simulation result shows that the method could detect clusters effectively and efficiently in both low and high dimensional data.
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2007, Vol. 20 
