An Adaptive k-Nearest Neighbors Clustering Algorithm for Complex Distribution Dataset

Authors:
Yan Zhang;Yan Jia;Xiaobin Huang;Bin Zhou;Jian Gu
Affiliations:
School of Computer, National University of Defense Technology, 410073 Changsha, China;School of Computer, National University of Defense Technology, 410073 Changsha, China;Department of Information Engineering, Air Force Radar Academy, 430019 Wuhan, China;School of Computer, National University of Defense Technology, 410073 Changsha, China;School of Computer, National University of Defense Technology, 410073 Changsha, China
Venue:
ICIC '07 Proceedings of the 3rd International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence
Year:
2009

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Abstract

To resolve the shortage of traditional clustering algorithm when dealing data set with complex distribution, a novel adaptive k-Nearest Neighbors clustering(AKNNC) algorithm is presented in this paper. This algorithm is made up of three parts: (a)normalize data set; (b)construct initial patterns; (c)merge initial patterns. Simulation results show that compared with classical FCA, our AKNNC algorithm not only has better clustering performance for data set with Complex distribution, but also can be applied to the data set without knowing cluster number in advance.