Abstract
A new tabu clustering method called ITCA is developed for the minimum sum of squares clustering problem, where DHB operation and mergence and partition operation are introduced to fine-tune the current solution and create the neighborhood, respectively. Compared with some known clustering methods, ITCA can obtain better performance, which is extensively demonstrated by experimental simulations.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Hall, L.O., Ozyurt, B., Bezdek, J.C.: Clustering with a genetically optimized approach. IEEE Trans. Evol. Comput. 3, 103–112 (1999)
Al-sultan, K.S.: A tabu search approach to the clustering problem. Pattern Recognit. 28, 1443–1451 (1995)
Bandyopadhyay, S., Maulik, U., Pakhira, M.K.: Clustering using simulated annealing with probabilisitc redistribution. Int. J. Pattern Recognit. Artif. Intell. 15, 269–285 (2001)
Krishna, K., Murty, M.N.: Genetic K-means algorithm. IEEE Trans. Syst. Man Cybern, Part B-Cybern. 29, 433–439 (1999)
Bandyopadhyay, S., Maulik, U.: An evolutionary technique based on K-means algorithm for optimal clustering in R N. Inf. Sci. 146, 221–237 (2002)
Zhang, Q.W., Boyle, R.D.: A new clustering algorithm with multiple runs of iterative procedures. Pattern Recognit. 24, 835–848 (1991)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Liu, Y., Zheng, D., Li, S., Wang, L., Chen, K. (2005). A Tabu Clustering Method with DHB Operation and Mergence and Partition Operation. In: Hoffmann, A., Motoda, H., Scheffer, T. (eds) Discovery Science. DS 2005. Lecture Notes in Computer Science(), vol 3735. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11563983_36
Download citation
DOI: https://doi.org/10.1007/11563983_36
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29230-2
Online ISBN: 978-3-540-31698-5
eBook Packages: Computer ScienceComputer Science (R0)