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A new kind of clustering algorithm called CABDET is presented in this paper. CABDET creates a tree structure for every cluster, from which the neighbor's radius of the current object is calculated by the local density of its father node. Those unprocessed objects in the neighbor of the current object are added to extend the tree structure until no new object is founded. Each density-tree is regarded as one cluster. CABDET requires only one input parameter as the initial radius of the root node and has no limitation of density threshold. Other characteristics include the abilities of discovering clusters with arbitrary shape and processing the noise data. The result of our experiments demonstrates that CABDET is significantly more accurate in discovering density-changeable clustering than the algorithm DBSCAN, and that CABDET is less sensitive to input parameters.
Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on (Volume:4 )
Date of Conference: 18-21 Aug. 2005