Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30069
Mining Correlated Bicluster from Web Usage Data Using Discrete Firefly Algorithm Based Biclustering Approach

Authors: K. Thangavel, R. Rathipriya

Abstract:

For the past one decade, biclustering has become popular data mining technique not only in the field of biological data analysis but also in other applications like text mining, market data analysis with high-dimensional two-way datasets. Biclustering clusters both rows and columns of a dataset simultaneously, as opposed to traditional clustering which clusters either rows or columns of a dataset. It retrieves subgroups of objects that are similar in one subgroup of variables and different in the remaining variables. Firefly Algorithm (FA) is a recently-proposed metaheuristic inspired by the collective behavior of fireflies. This paper provides a preliminary assessment of discrete version of FA (DFA) while coping with the task of mining coherent and large volume bicluster from web usage dataset. The experiments were conducted on two web usage datasets from public dataset repository whereby the performance of FA was compared with that exhibited by other population-based metaheuristic called binary Particle Swarm Optimization (PSO). The results achieved demonstrate the usefulness of DFA while tackling the biclustering problem.

Keywords: Biclustering, Binary Particle Swarm Optimization, Discrete Firefly Algorithm, Firefly Algorithm, Usage profile Web usage mining.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1093534

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1743

References:


[1] Cheng, Y., Church, G.M.: Biclustering of Expression Data. In: International Conference on Intelligent Systems for Molecular Biology, 2000, pp. 93–103.
[2] Coelho, G.P., de França, F.O., Von Zuben, F.J.: Multi-Objective Biclustering: When Nondominated Solutions are not Enough. J. Math. Model Algor. Vol. 8, 2009, pp:175–202.
[3] Das C, Maji P , Chattopadhyay S, A Novel Biclustering Algorithm for Discovering Value-Coherent Overlapping σ-Biclusters, Advanced Computing and Communications, 2008, pp:148-156,.
[4] David Beasley, David R. Bull, and Ralph R. Martin, An overview of genetic algorithms: Part 2, research topics. University Computing, Vol. 15, No. 4,1993, pp: 170–181.
[5] de Castro, P.A.D., de França, F.O., Ferreira, H.M., Von Zuben, F.J., Applying Biclustering to Perform Collaborative Filtering. In: International Conference on Intelligent System Design and Applications, 2007, pp. 421–426.
[6] de Castro, P.A.D., de França, F.O., Ferreira, H.M., Von Zuben, F.J. , Applying Biclustering to Text Mining: An Immune-Inspired Approach. In: de Castro, L.N., Von Zuben, F.J., Knidel, H. (eds.) ICARIS 2007. LNCS, vol. 4628, pp. 83–94. Springer, Heidelberg, 2007.
[7] de França, F.O., Coelho, G.P., Von Zuben, F.J. , bicACO: An Ant Colony Inspired
[8] Divina, F., Aguilar-Ruiz, J.S.: Biclustering of Expression Data with Evolutionary Computation. IEEE Trans. Knowl. Data Eng. Vol. 18, 2006, pp:590–602.
[9] Kennedy, J. and Eberhart, R.C., A discrete binary version of the particle swarm algorithm, Systems, Man, and Cybernetics, 1997. 'Computational Cybernetics and Simulation,IEEE International Conference ,vol.5,pp. :4104 - 4108 ,1997.
[10] Madeira, S.C., Oliveira, A.L.: Biclustering Algorithms for Biological Data Analysis: A Survey. IEEE/ACM Trans. Comput. Biol. Bioinform. Vol.1,, 2004, pp:24–45.
[11] Mitra, S., Banka, H. ,Multi-objective Evolutionary Biclustering of Gene Expression Data. Pattern Recogn. Vol. 39, 2006, pp: 2464–2477.
[12] Mobasher, B., Dai, H., Nakagawa, M., Luo, T., Discovery and evaluation of aggregate usage profiles for web personalization. Data Mining and Knowledge Discovery, Vol.6, 2002, pp. 61–82.
[13] R. Rathipriya, K. Thangavel, J. Bagyamani, Binary Particle Swarm Optimization based Biclustering of web Usage Data, International Journal Computer Applications, Vol. 25 No.2, , 2011, pp:43-49.
[14] R. Rathipriya, K. Thangavel, J. Bagyamani, Evolutionary Biclustering of Clickstream Data ,International Journal of Computer Science Issues, Vol. 8, 2011, pp:32-38.
[15] Smith, K.A. and A. Ng, Web Page Clustering using A Self-Organizing Map of User Navigation Patterns. Decision Support Systems, Vol. 35, 2003, pp : 245- 256.
[16] Srivastava, J., Cooley R., Deshpande, M., Tan, P.N., Web Usage Mining: Discovery and Applications of Usage Patterns from Web Data. SIGKDD Explorations, Vol. 1, No. 2, 2000, pp:12-23.
[17] Tanay, A., Sharan, R., Shamir, R.: Biclustering Algorithms: A Survey. In: Srinivas, A. (ed.) Handbook of Computational Molecular Biology, Chapman & Hall/CRC , 2005.
[18] Teng L, Chan L, Discovering biclusters by iteratively sorting with weighted correlation coefficient in gene expression data., J Signal Process Syst, Vol. 50, No. 3, 2008, pp:267-280.
[19] Xie, B., Chen, S., Liu, F.: Biclustering of Gene Expression Data Using PSO-GA Hybrid. In: International Conference Bioinformatics and Biomedical Engineering, pp. 302–305, 2007.
[20] Xu . R, Wunsch. D ,Survey of clustering algorithms, IEEE trans. on Neural Networks, vol. 16, No. 3, 2005, pp: 645-678.
[21] Yang, X-S., Firefly algorithms for multimodal optimization, in: Stochastic Algorithms: Foundations and Applications, SAGA, Lecture Notes in Computer Sciences, Vol. 5792, 2009, pp:169-178.