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Proceedings Paper

A fully distributed clustering algorithm based on fractal dimension
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Paper Abstract

Clustering or grouping of similar objects is one of the most widely used procedures in data mining, which has received enormous attentions and many methods have been proposed in these recent decades. However these traditional clustering algorithms require all the data objects to be located at one single site where it is analyzed. And such limitation cannot face the challenge as nowadays monstrous sizes of data sets are often stored on different independently working computers connected to each other via local or wide area networks instead of one single site. Therefore in this paper, we propose a fully distributed clustering algorithm, called a fully distributed clustering based on fractal dimension (FDCFD), which enables each site to collaborate in forming a global clustering model with low communication cost. The main idea behind FDCFD is via calculating fractal dimension to group points in a cluster in such a way that none of the points in the cluster changes the cluster's fractal dimension radically. In our theoretical analysis, we will demonstrate that our approach can work very well for clustering data that is inherently distributed, collect information spread over several local sites to form a global clustering meanwhile without communication costs and delays for transmitting.

Paper Details

Date Published: 10 September 2007
PDF: 8 pages
Proc. SPIE 6773, Next-Generation Communication and Sensor Networks 2007, 67730R (10 September 2007); doi: 10.1117/12.752076
Show Author Affiliations
Xiao Xiong, Tianjin Univ. (China)
Jie Zhang, Tianjin Univ. (China)
Qingwei Shi, Tianjin Univ. (China)

Published in SPIE Proceedings Vol. 6773:
Next-Generation Communication and Sensor Networks 2007
Sergey I. Balandin, Editor(s)

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