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DOFL: Kernel Based Directive Operative Fractional Lion Optimisation Algorithm for Data Clustering

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In recent years, clustering finds various applications in most of the fields like networking, telecommunications and medical domain. So, various clustering algorithms are developed by researchers to improve the clustering performance. Among this, optimisation based clustering algorithm is one of the recently developed algorithms for clustering process to discover the optimal clusters based on the objective function. Accordingly, in this paper, we have proposed directive operative fractional lion optimization using kernel based clustering algorithm. Initially, three objective functions are developed based on the kernel function. Then, the result of objective function is selected as the fitness function for the proposed optimisation algorithm called directive operative based fractional lion algorithm to select the rapid centroid of the data cluster. Finally, the performance of the proposed kernel based directive operative fractional lion algorithm (DOFL) algorithm is evaluated based on the various evaluation metrics such as cluster accuracy, jaccard coefficient and rand coefficient using the both benchmarked iris and wine data sets. From the research outcome, we can prove that, the maximum clustering accuracy of 79% is obtained by the proposed optimization clustering algorithm compared to other existing clustering algorithms such as Particle Swarm clustering algorithm (PSC), modified Particle Swarm clustering algorithm (mPSC), lion algorithm and fractional lion algorithm.
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Clustering; Optimisation; Directive Operative Based Searching Algorithm; Objective Function; Kernel Based Clustering

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C.C. Hsu, Y.C. Chen, Mining of mixed data with application to catalog marketing, Expert Systems with Applications, vol. 32, no. 1, pp.12–27, 2007.

Jinchao Ji , Wei Pang , Chunguang Zhou, Xiao Han, Zhe Wang, "A fuzzy k-prototype clustering algorithm for mixed numeric and categorical data", Knowledge-Based Systems, vol. 30, pp.129–135, 2012.

Anil K. Jain and Richard C. Dubes, “Algorithms for Clustering Data”, Prentice-Hall International, 1988.

Kaufman L, P. Rousseuw, “Finding Groups in Data- An Introduction to Cluster Analysis”, Wiley Series in Probability and Math Sciences, 1990.

Michael R. Anderberg, “Cluster analysis for applications”, Academic Press, 1973

H.Venkateswara Reddy, S.Viswanadha Raju "Data Labeling method based on Cluster Purity using Relative Rough Entropy for Categorical Data Clustering", In Proceedings of International Conference on Advances in Computing, Communications and Informatics , pp. 500 - 506, 2013.

A K Jain, MN Murthy and P J Flyn, “Data Clustering: A Review,” ACM Computing Survey, 1999.

M. Ester, H.-P. Kriegel, J. Sander, and X. Xu, “A density-based algorithm for discovering clusters in large spatial databases with noise,” In Proceedings of International Conference on Knowledge Discovery and Data Mining (KDD), pp. 226–231, 1996.

Walid A. Omran; Mehrdad Kazerani; Magdy M. A. Salama,"A Clustering-Based Method for Quantifying the Effects of Large On-Grid PV Systems", IEEE Transactions on Power Delivery, vol. 25, no. 4, pp. 2617 - 2625, 2010.

Han,J. and Kamber,M. “Data Mining Concepts and Techniques”, Morgan Kaufmann, 2001.

Gibson. D., Kleinberg. J.M., Raghavan. P. “Clustering Categorical Data an Approach Based on Dynamical Systems”, Very Large Database, vol. 8, pp. 222-236, 2000.

D. Binu, "Cluster analysis using optimization algorithms with newly designed objective functions", Expert Systems with Applications, vol. 42, no. 14, pp. 5848–5859, 2015.

Demidova, L., Sokolova, Y., Nikulchev, E., Use of Fuzzy Clustering Algorithms Ensemble for SVM Classifier Development, (2015) International Review on Modelling and Simulations (IREMOS), 8 (4), pp. 446-457.

Rammohan, N., Baburaj, E., Genetic Clustering with Workload Multi-task Scheduler in Cloud Environment, (2014) International Journal on Communications Antenna and Propagation (IRECAP), 4 (3), pp. 77-86.

Graves, D., Pedrycz, W., kernel based fuzzy clustering and fuzzy clustering: A comparative experimental study, Fuzzy sets and systems, vol. 161, pp. 522-543, 2010.

Mualik, U., & Bandyopadhyay, S. “Genetic algorithm based clustering technique” Pattern Recognition”, vol. 33, pp. 1455–1465, 2002.

Premalatha, K., & Natarajan, A. M., “A new approach for data clustering based on PSO with local search”, Computer and Information Science, vol. 1, no. 4, 2008.

Zhang, C., Ouyang, D., & Ning, J., “An artificial bee colony approach for clustering”, Expert Systems with Applications, vol. 37, pp. 4761–4767, 2010.

Castellanos-Garzon, J. A., & Diaz, F, “An evolutionary computational model applied to cluster analysis of DNA microarray data. Expert Systems with Applications, vol. 40, no. 7, pp. 2575–2591, 2013.

Senthilnath, J., Omkar, S. N., & Mani, V., Clustering using firefly algorithm: Performance study, Swarm and Evolutionary Computation, vol. 1, pp. 164–171. 2011.

B. Rajakumar, "The Lion׳s Algorithm: a new nature-inspired search algorithm", In Proceedings of International conference on communication, computing and security, vol. 6, pp. 126–135, 2012.

Mahdieh Motamedi et al., "Data Clustering Using Kernel Based Algorithm", Information Technology, Control and Automation, vol. 4, no. 3, 2014.

Xuesong Yin, SongcanChen,Enliang Hu, Daoqiang Zhang, "Semi-supervised clustering with metric learning: An adaptive kernel method", Pattern Recognition, vol. 43, pp. 1320–1333,2010.

Binu D, et al. "MKF-Cuckoo: Hybridization of Cuckoo Search and Multiple Kernel-Based Fuzzy C-Means Algorithm", In proceedings of AASRI Conference on Intelligent Systems and Control, vol. 4, pp. 243-249, 2013.

M.C. Naldi, R.J.G.B. Campell, "Evolutionary k-means for distributed data sets", Neurocomputing, vol. 127, pp. 30–42, 2014.

PradiptaMaji, "Fuzzy–Rough Supervised Attribute Clustering Algorithm and Classification of Microarray Data", IEEE transactions on systems, man, and cybernetics—part b: cybernetics, Vol. 41, No. 1, pp. 222-253, February 2011.

Jonathon K. Parker, and Lawrence O. Hall, "Accelerating Fuzzy-C Means Using an Estimated Subsample Size", IEEE transactions on fuzzy systems, vol. 22, No. 5, pp. 1229-1244, 2014.

Satish Chander, Vijaya P, Praveen Dhyani, "Fractional Lion algorithm- An Optimization algorithm for data clustering", Journal of computer science, 2016.

Li, Qj, "Spatial kernel k-harmonic means clustering for multi-spectral image segmentation, IET image processing, vol. 1, no. 2, pp. 156-167, 2007.

Lichman, M. (2013). UCI Machine Learning Repository []. Irvine, CA: University of California, School of Information and Computer Science.

Mitchell Yuwono, Steven W. Su, Bruce D. Moulton, and Hung T. Nguyen, "Data Clustering Using Variants of Rapid Centroid Estimation", IEEE transactions on evolutionary computation, VOL. 18, NO. 3, pp. 366-377, JUNE 2014.

Miao Wan, Lixiang Li, Jinghua Xiao, Cong Wang, Yixian Yang, "Data clustering using bacterial foraging optimization", J IntellInfSyst, vol. 38, pp. 321–341,2012.


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