Abstract
This paper proposes an extension to the original GA-clustering algorithm by introducing a new way to mutate the chromosome. The new mutation operator takes the previous values of the chromosome into account when mutating the chromosome. The superiority of the proposed approach over the original GA-clustering algorithm and K-means algorithm is demonstrated by using 6 benchmark data sets.
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References
Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers, San Francisco (2001)
Maulik, U., Bandyopadhyay, S.: Genetic Algorithm-based Clustering Technique. Pattern Recognition 33, 1455–1465 (2000)
Blake, C.L., Merz, C.J.: UCI Repository of Machine Learning Databases: http:// www.ics.uci.edu/~mlearn/MLRepository.html. University of California, Department of Information and Computer Science. (1998)
Statlog Project Datasets: http://www.liacc.up.pt/ML/statlog/datasets/heart/. (2003)
Deterding, D.H.: Speaker Normalization for Automatic Speech Recognition. Ph.D. Dissertation. (1989)
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© 2006 Springer-Verlag Berlin Heidelberg
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Thammano, A., Kakulphimp, U. (2006). Genetic Algorithm-Based Clustering and Its New Mutation Operator. In: Huang, DS., Li, K., Irwin, G.W. (eds) Intelligent Computing. ICIC 2006. Lecture Notes in Computer Science, vol 4113. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11816157_85
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DOI: https://doi.org/10.1007/11816157_85
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37271-4
Online ISBN: 978-3-540-37273-8
eBook Packages: Computer ScienceComputer Science (R0)