Grid-based clustering algorithm based on intersecting partition and density estimation

Authors:
Bao-Zhi Qiu;Xiang-Li Li;Jun-Yi Shen
Affiliations:
School of Information & Engineering, Zhengzhou University, Zhengzhou, China;School of Information & Engineering, Zhengzhou University, Zhengzhou, China;School of Electronic Information & Engineering, Xi'an Jiaotong University, Xi'an, China
Venue:
PAKDD'07 Proceedings of the 2007 international conference on Emerging technologies in knowledge discovery and data mining
Year:
2007

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Abstract

In order to solve the problem that traditional grid-based clustering techniques lack of the capability of dealing with data of high dimensionality, we propose an intersecting grid partition method and a density estimation method. The partition method can greatly reduce the number of grid cells generated in high dimensional data space and make the neighbor-searching easily. On basis of the two methods, we propose grid-based clustering algorithm (GCOD), which merges two intersecting grids according to density estimation. The algorithm requires only one parameter and the time complexity is linear to the size of the input data set or data dimension. The experimental results show that GCOD can discover arbitrary shapes of clusters and scale well.