Adaptive K-means clustering algorithm

Hailin Chen; Xiuqing Wu; Junhua Hu

doi:10.1117/12.750002

15 November 2007 Adaptive K-means clustering algorithm

Hailin Chen, Xiuqing Wu, Junhua Hu

Proceedings Volume 6788, MIPPR 2007: Pattern Recognition and Computer Vision; 67882A (2007) https://doi.org/10.1117/12.750002
Event: International Symposium on Multispectral Image Processing and Pattern Recognition, 2007, Wuhan, China

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.

Citation Download Citation

Hailin Chen, Xiuqing Wu, and Junhua Hu "Adaptive K-means clustering algorithm", Proc. SPIE 6788, MIPPR 2007: Pattern Recognition and Computer Vision, 67882A (15 November 2007); https://doi.org/10.1117/12.750002

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available