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Fast Implementation of Dual Clustering Algorithm for Spatial Data Mining

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4 Author(s)

Many applications scenarios require spatial clustering results in which a cluster has not only high proximity in geometrical domain but also high similarity in non-geometrical domain. Such clustering problem is called dual clustering. We proposed a new algorithm for solving such problem. We first implemented density-based sampling on spatial dataset to reduce data size, and then we partitioned the sample to different clusters in such a way that each cluster forms a compact region in geometrical domain while has the similarity in non-geometrical domain. The experimental results show our algorithm is very effective and efficient.

Published in:

Fuzzy Systems and Knowledge Discovery, 2007. FSKD 2007. Fourth International Conference on  (Volume:3 )

Date of Conference:

24-27 Aug. 2007