Abstract
We consider the problem of partitioning a data set of n data objects into c homogeneous subsets (that is, data objects in the same subset should be similar to each other), such that each subset is of approximately the same size. This problem has applications wherever a population has to be distributed among a limited number of resources and the workload for each resource shall be balanced. We modify an existing clustering algorithm in this respect, present some empirical evaluation and discuss the results.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
References
Jain, A.K., Dubes, R.C.: Algorithms for Clustering Data. Prentice-Hall, Englewood Cliffs (1988)
Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York (1981)
Höppner, F., Klawonn, F., Kruse, R., Runkler, T.A.: Fuzzy Cluster Analysis. John Wiley & Sons, Chichester (1999)
Klawonn, F., Höppner, F.: What is fuzzy about fuzzy clustering? – Understanding and improving the concept of the fuzzifier. In: Advances in Intelligent Data Analysis, pp. 254–264. Springer, Heidelberg (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Klawonn, F., Höppner, F. (2006). Equi-sized, Homogeneous Partitioning. In: Gabrys, B., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2006. Lecture Notes in Computer Science(), vol 4252. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893004_9
Download citation
DOI: https://doi.org/10.1007/11893004_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46537-9
Online ISBN: 978-3-540-46539-3
eBook Packages: Computer ScienceComputer Science (R0)