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Proceedings Paper

Adaptive K-means clustering algorithm
Author(s): Hailin Chen; Xiuqing Wu; Junhua Hu
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Paper Abstract

Clustering is a fundamental problem for a great variety fields such as pattern recognition, computer vision. A popular technique for clustering is based on K-means. However, it suffers from the four main disadvantages. Firstly, it is slow and scales poorly on the time. Secondly, it is often impractical to expect a user to specify the number of clusters. Thirdly, it may find worse local optima. Lastly, its performance heavily depends on the initial clustering centers. To overcome the above four disadvantages simultaneously, an effectively adaptive K-means clustering algorithm (AKM) is proposed in this paper. The AKM estimates the correct number of clusters and obtains the initial centers by the segmentation of the norm histogram in the linear normed space consisting of the data set, and then performs the local improvement heuristic algorithm for K-means clustering in order to avoid the local optima. Moreover, the kd-tree is used to store the data set for improving the speed. The AKM was tested on the synthetic data sets and the real images. The experimental results demonstrate the AKM outperforms the existing methods.

Paper Details

Date Published: 15 November 2007
PDF: 9 pages
Proc. SPIE 6788, MIPPR 2007: Pattern Recognition and Computer Vision, 67882A (15 November 2007); doi: 10.1117/12.750002
Show Author Affiliations
Hailin Chen, Univ. of Science and Technology of China (China)
Xiuqing Wu, Univ. of Science and Technology of China (China)
Junhua Hu, Univ. of Science and Technology of China (China)

Published in SPIE Proceedings Vol. 6788:
MIPPR 2007: Pattern Recognition and Computer Vision
S. J. Maybank; Mingyue Ding; F. Wahl; Yaoting Zhu, Editor(s)

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