Using ant colony optimization for efficient clustering

Yong Wang; Wei Zhang; Jun Chen; Jianfu Li; Li Xiao

doi:10.1117/12.784045

9 January 2008 Using ant colony optimization for efficient clustering

Yong Wang, Wei Zhang, Jun Chen, Jianfu Li, Li Xiao

Author Affiliations +

Proceedings Volume 6794, ICMIT 2007: Mechatronics, MEMS, and Smart Materials; 67944C (2008) https://doi.org/10.1117/12.784045
Event: ICMIT 2007: Mechatronics, MEMS, and Smart Materials, 2007, Gifu, Japan

Abstract

To improve the performance of data clustering, this study proposes a novel clustering method called ABCA (ACO Based Clustering Algorithm). The presented method is based on heuristic concept and using Ant Colony Optimization algorithm (ACO) to obtain global search. The main advantage of these algorithms lies in the fact that no additional information, such as an initial partitioning of the data or the number of clusters, is needed. Since the proposed method is very efficiently, thus it can perform data clustering very quickly.

Citation Download Citation

Yong Wang, Wei Zhang, Jun Chen, Jianfu Li, and Li Xiao "Using ant colony optimization for efficient clustering", Proc. SPIE 6794, ICMIT 2007: Mechatronics, MEMS, and Smart Materials, 67944C (9 January 2008); https://doi.org/10.1117/12.784045

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