Abstract
Existing clustering techniques have many drawbacks and this includes being trapped in a local optima. In this paper, we introduce the utilization of a new meta-heuristics algorithm, namely the Firefly algorithm (FA) to increase solution diversity. FA is a nature-inspired algorithm that is used in many optimization problems. The FA is realized in document clustering by executing it on Reuters-21578 database. The algorithm identifies documents that has the highest light intensity in a search space and represents it as a centroid. This is followed by recognizing similar documents using the cosine similarity function. Documents that are similar to the centroid are located into one cluster and dissimilar in the other. Experiments performed on the chosen dataset produce high values of Purity and F-measure. Hence, suggesting that the proposed Firefly algorithm is a possible approach in document clustering.
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References
Das, S., Abraham, A., Konar, A.: Metaheuristic Clustering, Springer, Heidelberg (2009).
AnithaElavarasi, S., Akilandeswari, J., Sathiyabma, B.: A survay on Partition Clustering Algorithms. In: International journal of Enterprise Computing and Business Systems, vol. 1, issue 1, (2011).
Ye, N., Gauch, S., Wang, Q., Luong, H.:An Adaptive Ontology based Hierarchical Browsing System for CiteSeerX. In: Second International Conference on Knowledge and Systems Engineering (KSE), pp. 203–208, IEEE, (2010).
Wilson, H., Boots, B., Millward, A. A.: A Comparison of Hierarchical and Partitional Clustering Techniques for Multispectral Image Classification. vol.3, pp. 1624-1626, (2002).
Xu, Y.: Hybrid clustering with application to web mining. In: Proceedings of the International Conference on Active Media Technology (AMT 2005), pp. 574–578,IEEE, (2005).
Aliguliyev, R. M.: Clustering of Document Collection- A Weighted Approach. In: Expert Systems with Applications,vol. 36, issue 4, pp. 7904–7916,Elsevier, (2009).
Boley, D.: Principal Direction Divisive Partitioning. In: Data Mining and Knowledge Discovery, vol. 2, issue. 4, pp. 325 – 344, ACM, (1998).
Feng, L., Qiu, M.H., Wang, Y.X., Xiang, Q.L., Yang, Y.F., Liu, K. A.: Fast Divisive Clustering Algorithm Using an Improved Discrete Particle Swarm Optimizer. In:Pattern Recognition Letters, vol. 31, issue. 11, pp. 1216-1225,Elsevier, (2010).
Rana, S., Jasola,S., Kumar,R.: A Hybrid Sequential Approach for Data Clustering using K-means and Particle Swarm Optimization Algorithm. In: International Journal of Engineering, Science and Technology,vol. 2, No. 6, pp. 167-176, (2010).
Bache, K., Lichman, M.: UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science,(2013).
Yang,X. S.: Nature-inspired Metaheuristic Algorithms, 2nd ed., Luniver press, United Kingdom, (2011).
Horng, M. H., Jiang, T. W.: Multilevel Image Thresholding Selection based on theFirefly Algorithm. In: 7th International Conference on Ubiquitous Intelligence & Computing and 7th International Conference on Autonomic & Trusted Computing (UIC/ATC), pp. 58 – 63, IEEE, (2010).
Senthilnath, J., Omkar, S. N., Mani, V.: Clustering Using Firefly Algorithm: Performance Study. In: Swarm and Evolutionary Computation, vol. 1, issue. 3, pp. 164-171, Elsevier, (2011).
Hassanzadeh, T., Meybodi, M. R.:A New Hybrid Approach for Data Clustering Using Firefly Algorithm and K-means. In: 16thIEEECSI International Symposium on Artificial Intelligence and Signal Processing (AISP), pp. 007 – 011, (2012).
Abshouri, A. A., Bakhtiary,A.: A New Clustering Method Based on Firefly and KHM. In: Journal of Communication and Computer, vol. 9, pp. 387-391, (2012).
Xu, G., Zhang,Y., Li, L.: Web mining and social networking, Techniques and application, New York, Springer, (2011).
Manning, C. D., Raghavan,P., Schütze,H.: Introduction to Information Retrieval, 1 ed., Cambridge University Press, (2008).
Lewis,D.: The reuters-21578 text categorizationtest collection, 1999.[Online].Available:http://kdd.ics.uci.edu/database/reuters21578/reuters21578.html.
Murugesan, K, Zhang,J.: Hybrid Bisect K-means Clustering Algorthim. In: IEEE International Conference on Business Computing and Global Informatization (BCGIN), pp. 216 – 219, IEEE, (2011).
Meghabghab, G., Kandel, A.: Search Engines,Link Analysis,and User’s Web Behaviour, Berlin Heidelberg: Springer-Verlag, (2008).
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Mohammed, A.J., Yusof, Y., Husni, H. (2014). Weight-Based Firefly Algorithm for Document Clustering. In: Herawan, T., Deris, M., Abawajy, J. (eds) Proceedings of the First International Conference on Advanced Data and Information Engineering (DaEng-2013). Lecture Notes in Electrical Engineering, vol 285. Springer, Singapore. https://doi.org/10.1007/978-981-4585-18-7_30
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DOI: https://doi.org/10.1007/978-981-4585-18-7_30
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