A harmony search algorithm for clustering with feature selection
DOI:
https://doi.org/10.17533/udea.redin.14724Keywords:
harmony search, clustering, feature selectionAbstract
This paper presents a new clustering algorithm, called IHSK, with feature selection in a linear order of complexity. The algorithm is based on the combination of the harmony search and K-means algorithms. Feature selection uses both the concept of variability and a heuristic method that penalizes the presence of dimensions with a low probability of contributing to the current solution. The algorithm was tested with sets of synthetic and real data, obtaining promising results.
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A. K. Jain, M. N. Murty, P. J. Flynn. “Data clustering: a review”. ACM Comput. Surv. Vol. 31. 1999. pp. 264- 323. DOI: https://doi.org/10.1145/331499.331504
K. Jacob, N. Charles, T. Marc. Grouping Multidimensional Data Recent Advances in Clustering. Ed. Springer-Verlag. New York. 2006. pp. 25-72.
J. Dy, G.C.E. Brodley, J. Mach. “Feature Selection for Unsupervised Learning”. Learn. Res. Vol.5. 2004. pp. 845-889.
Z. Geem, J. Kim, G.V. Loganathan. “A New Heuristic Optimization Algorithm”. Harmony Search Simulation. Vol.76. 2001. pp. 60-68. DOI: https://doi.org/10.1177/003754970107600201
M. G. H Omran, M. Mahdavi. “Global-best harmony search”. Applied Mathematics and Computation, Vol. 198. 2008. pp. 643-656. DOI: https://doi.org/10.1016/j.amc.2007.09.004
M. Mahdavi, M. Fesanghary, E. Damangir. “An improved harmony search algorithm for solving optimization problems”. Applied Mathematics and Computation. Vol. 188. 2007. pp. 1567-1579. DOI: https://doi.org/10.1016/j.amc.2006.11.033
S. J. Redmondand, C. Heneghan. “A method for initialising the K-means clustering algorithm using kd-trees”. Pattern Recognition Letters. Vol. 28. 2007. pp. 965-973. DOI: https://doi.org/10.1016/j.patrec.2007.01.001
A. K Jain, R.C. Dubes. Algorithms for clustering data. Ed. Prentice-Hall Inc. Englewood Cliffs (NJ.). 1988. pp.143-222.
A. Webb. Statistical Pattern Recognition. 2ª ed. Ed. John Wiley & Sons. Malvern (UK) 2002. pp. 361- 408. DOI: https://doi.org/10.1002/0470854774
A. L. Blum, P. Langley. “Selection of relevant features and examples in machine learning”. Artificial Intelligence. Vol. 97. 1997. pp. 245-271. DOI: https://doi.org/10.1016/S0004-3702(97)00063-5
K. Ron, H. J. George. “Wrappers for feature subset selection”. Artif. Intell. Vol. 97. 1997. pp. 273-324. DOI: https://doi.org/10.1016/S0004-3702(97)00043-X
H. Zeng, Y. M. Cheung. “A new feature selection method for Gaussian mixture clustering”. Pattern Recognition. Vol. 42. 2009. pp. 243-250. DOI: https://doi.org/10.1016/j.patcog.2008.05.030
S. Osiński, J. Stefanowski, D. Weiss. “Lingo search results clustering algorithm based on Singular Value Decomposition”. International Conference on Intelligent Information Systems (IIPWM). Zakapore (Poland). 2004. pp. 359-397. DOI: https://doi.org/10.1007/978-3-540-39985-8_37
J. Han, M. Kamber. Data Mining Concepts and Techniques. 2ª ed. Ed.Morgan Kaufmann Publishers. 2006.pp.71-72.
S. Weiguo, L. Xiaohui, M. Fairhurst. “A Niching Memetic Algorithm for Simultaneous Clustering and Feature Selection”. IEEE Transactions on Knowledge and Data Engineering. Vol. 20. 2008. pp. 868-879. DOI: https://doi.org/10.1109/TKDE.2008.33
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