AM  Vol.5 No.8 , May 2014
Automatic Clustering Using Teaching Learning Based Optimization
ABSTRACT

Finding the optimal number of clusters has remained to be a challenging problem in data mining research community. Several approaches have been suggested which include evolutionary computation techniques like genetic algorithm, particle swarm optimization, differential evolution etc. for addressing this issue. Many variants of the hybridization of these approaches also have been tried by researchers. However, the number of optimal clusters and the computational efficiency has still remained open for further research. In this paper, a new optimization technique known as “Teaching-Learning-Based Optimization” (TLBO) is implemented for automatic clustering of large unlabeled data sets. In contrast to most of the existing clustering techniques, the proposed algorithm requires no prior knowledge of the data to be classified rather it determines the optimal number of partitions of the data “on the run”. The new AUTO-TLBO algorithms are evaluated on benchmark datasets (collected from UCI machine repository) and performance comparisons are made with some well-known clustering algorithms. Results show that AUTO-TLBO clustering techniques have much potential in terms of comparative results and time of computations.


Cite this paper
Murty, M. , Naik, A. , Murthy, J. , Reddy, P. , Satapathy, S. and Parvathi, K. (2014) Automatic Clustering Using Teaching Learning Based Optimization. Applied Mathematics, 5, 1202-1211. doi: 10.4236/am.2014.58111.
References
[1]   Jain, A.K., Murty, M.N. and Flynn, P.J. (1999) Data Clustering: A Review. ACM Computing Surveys, 31, 264-323.
http://dx.doi.org/10.1145/331499.331504

[2]   Chou, C.H., Su, M.C. and Lai, E. (2004) A New Cluster Validity Measure and Its Application to Image Compression. Pattern Analysis and Applications, 7, 205-220.
http://dx.doi.org/10.1007/s10044-004-0218-1

[3]   Storn, R. and Price, K. (1997) Differential Evolution—A Simple and Efficient Heuristic for Globaloptimization over Continuous spaces. Journal of Global Optimization, 11, 341-359. http://dx.doi.org/10.1023/A:1008202821328

[4]   Das, S., Abraham, A. and Konar, A. (2008) Automatic Clustering Using an Improved Differential Evolution Algorithm. IEEE Transactions on Systems, Man, and Cybernetics—Part A: Systems and Humans, 38, 218-237.

[5]   Day, W.H. and Edelsbrunner, H. (1984) Efficient Algorithms for Agglomerative Hierarchical Clustering Methods. Journal of Classification, 1, 1-24.
http://dx.doi.org/10.1007/BF01890115

[6]   Bandyopadhyay, S. and Maulik, U. (2002) Genetic Clustering for Automatic Evolution of Clusters and Application to Image Classification. Pattern Recognition, 35, 1197-1208.
http://dx.doi.org/10.1016/S0031-3203(01)00108-X

[7]   Omran, M., Salman, A. and Engelbrecht, A. (2005) Dynamic Clustering Using Particle Swarm Optimization with Application in Unsupervised Image Classification. Proceedings of the 5th World Enformatika Conference (ICCI), Prague.

[8]   Clerc, M. and Kennedy, J. (2002) The Particle Swarm-Explosion, Stability and Convergence in a Multi-Dimensional Complex Space. IEEE Transactions on Evolutionary Computation, 6, 58-72.

[9]   Kennedy, J., Eberhart, R.C. and Shi, Y. (2001) Swarm Intelligence. Morgan Kaufmann Publishers, San Francisco.

[10]   Blake, C., Keough, E. and Merz, C.J. (1998) UCI Repository of Machine Learning Database.
http://www.ics.uci.edu/~mlearn/MLrepository.html

[11]   Raghavan, V.V., Birchand, K., Paterlinia, S. and Krink, T. (2006) Differential Evolution and Particle Swarm Optimization in Partitional Clustering. Computational Statistics & Data Analysis, 50, 1220-1247.
http://dx.doi.org/10.1016/j.csda.2004.12.004

[12]   Pal, S.K. and Majumder, D.D. (1977) Fuzzy Sets and Decision Making Approaches in Vowel and Speaker Recognition. IEEE Transactions on Systems, Man, and Cybernetics, 7, 625-629.

[13]   Satapathy, S.C. and Naik, A. (2011) Data Clustering Based on Teaching Learning Based Optimization. Lecture Notes in Computer Science, 7077, 148-156.

[14]   Murty Ramakrishna, M., Murthy, J.V.R., Prasad Reddy, P.V.G.D., Naik, A. and Satapathy, S.C. (2013) Performance of Teaching Learning Based Optimization Algorithm with Various Teaching Factor Values for Solving Optimization Problems. Proceedings of the International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) 2013, Advances in Intelligent Systems and Computing, 247, 207-216.

[15]   Rao, R.V., Savsani, V.J. and Vakharia, D.P. (2011) Teaching-Learning-Based Optimization: A Novel Method for Constrained Mechanical Design Optimization Problems. Computer-Aided Design, 43, 303-315.
http://dx.doi.org/10.1016/j.cad.2010.12.015

[16]   Flury, B. (1997) A First Course in Multivariate Statistics. Springer-Verlag, Berlin.
http://dx.doi.org/10.1007/978-1-4757-2765-4

[17]   Omran, M., Engelbrecht, A. and Salman, A. (2005) Particle Swarm Optimization Method for Image Clustering. International Journal of Pattern Recognition and Artificial Intelligence, 19, 297-322.

[18]   Olson, C. (1995) Parallel Algorithms for Hierarchical Clustering. Parallel Computing, 21, 1313-1325.
http://dx.doi.org/10.1016/0167-8191(95)00017-I

 
 
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