Asian Journal of Computer Science and Technology (AJCST)
Multi-Objective Optimization to Identify High Quality Clusters with Close Referential Point using Evolutionary Clustering Techniques
Author : M. AnushaVolume 7 No.3 October-December 2018 pp 68-71
Abstract
Most of the real-world optimization problems have multiple objectives to deal with. Satisfying one objective at a time may lead to the huge deviation in other. This paper uses criterion knowledge ranking algorithm solving multi-objective optimization problems. The aim of this research paper is to solve a multi-objective optimization algorithm with close reference point learning method to identify high quality data clusters. A Simple crossover measure is used to quantify the diversity of the whole set, by considering all patterns as a complete entity. In this paper, the task of identifying high quality data clusters using close reference points is proposed to solve multi-objective optimization problem using evolutionary clustering techniques. The proposed algorithm finds the closest feature from the selected features of the data sets that also minimizes the cost while maintains the quality of the solution by producing better convergence. The resultant clusters were analysed and validated using cluster validity indexes. The proposed algorithm is tested with several UCI real-life data sets. The experimental results substantiates that the algorithm is efficient and robust.
Keywords
Multi-Objective Optimization, Reference Point Learning, Evolutionary Clustering, Feature Selection, High Quality Data Clusters
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References
[1] H.R.Cheshmehgaz, H.Haron, and A.Sharifi, “The review of multiple evolutionary searchs and multi-objecive evolutionary algorithm”, Artificial Intelligence, pp. 1-33, 2013.
[2] O.Schutze, M.Laumanns, C.A.C.Collo and E.G.Talbi, “Compting gap free pareto front approximations with stochastic search algorithms”, Evolu.Compt., Vol. 18,No. 1,pp. 65-96, 2010.
[3] Sk.M.Islam, S.Das, S.Ghosh, S.Roy and P.N.Suganthan, “An adaptive differential evolution algorithm with novel mutation andcrossover strategies for global numerical optimization”, IEEE. Trans. SMC., Vol. 12, No. 2, pp. 282-500, 2012.
[4] Wu, Le, Liu, Qi, Chen, Enhong, Yuan, Nicholas Jing, Guo, Guangming, Xie, Xing, “Relevance meets coverage: A unified framework to generate diversified recommendations”, ACM Trans. Intell. Syst. Technol. Vol.7, No.3, pp. 39.1-39.30, 2016
[5] Yin, Junfu, Zheng, Zhigang, Cao, Longbing, Song, Yin, Wei, Wei, “Efficiently mining top-k high utility sequential patterns”, In: IEEE International Conference on DataMining, pp. 1259–1264. 2013.
[6] Zihayat, Morteza, An, Aijun, “Mining top-k high utility patterns over data streams”,Inf. Sci, Vol.285,No.20,pp.138–161, 2014.
[7] Ryang, Heungmo, Yun, Unil, “Top-k high utility pattern mining with effective threshold raising strategies” Knowl.-Based Syst, Vol.76, pp.109–126. 2015.
[8] Tseng, S. Vincent Wu, Chengwei, Fournierviger, Philippe, Yu, Philip S., “Efficient algorithms for mining top-k high utility itemsets”, IEEE Trans. Knowl. Data Eng, Vol. 28, No. 1, pp. 54–67. 2016.
[9] Hammar, Mikael, Karlsson, Robin, Nilsson, Bengt J., “Using maximum coverage to optimize recommendation systems in e-commerce.”, Proceedings of the 7th ACM Conference on Recommender Systems, pp. 265–272, 2013.
[10] Lucas, Tarcsio, Silva, Tlio C. P.B., Vimieiro, Renato, Ludermir, Teresa B., “A new evolutionary algorithm for mining top-k discriminative patterns in high dimensional data.” Appl. Soft Comput, Vol. 59, pp. 487-499. 2017.
[11] Rui Wang, Peter J.Fleming, and Robin C. Purshouse “General framework for localised multi-objective evolutionary algorithms”, Information Sciences, Elsevier, pp. 29-53, 2014.
[12] Alvaro Gracia-Piquer, Albert Fornells, Jaume Bacardit, Albert Orriols and Elisabet Golobardes,“Large-Scale Experimental Evaluation of Cluster Representations for Multiobjective Evolutionary Clustering”, IEEE Transcations on Evolutionary Computation, pp. 36-53, 2014.
[13] Hu Xia, Jian Zhuang, Dehong Yu, “Novel soft subspace clustering with multi-objective evolutionary approach for high- dimensional data”, Pattern Recognition, Elsevier, pp.2562-2575, 2013.
[14] Sujoy Chatterjee and Anirban Mukhopadhyay, “Clustering Ensemble: A Multiobjective Genetic Algorithm based Approach”, Procedia Technology, Elsevier, pp. 443-449, 2013.
[15] Shi-Zheng Zhao, Ponnuthurai Nagaratnam Suganthan and Qingfu, “Decomposition–Based Multiobjective Evolutionary Algorithm with an Ensemble of Neighborhood Sizes”, IEEE Transactions on Evolutionary Computation, pp. 442-446, 2012.
[16] Gema Bello-Orgaz, and David Camacho, “Evolutionary clustering algorithm for community detection using graph-based information”, IEEE Cong.Evolu.Compt., pp. 930-937. 2014.
[17] Hemant Kumar Singh, Amitay Isaacs,and Tapabrata Ray, “A pareto corner search evolutionary algorithm and dimensionality reduction in
many-objective optimization problem”, IEEE Trans.Evolu.Compt., Vol. 15, No. 4, pp. 539-556, 2011.
[18] M.Anusha and J.G.R.Sathiaseelan, “An Improved K-Means Genetic Algorithm for Multi-objective Optimization”, International Journal of Applied Engineering Research,pp. 228-231, 2015.
[19] M.Anusha and J.G.R.Sathiaseelan, “An Empirical Study on Multi-Objective Genetic Algorithms using Clustering Techniques”, International Journal of Advanced Intelligence Paradigms. Vol. 8, No. 3, pp. 343-354, UK, 2016.
[20] M.Anusha and J.G.R .Sathiaseelan, “Feature Selection using K-Means Genetic Algorithm for Multi-objective Optimization”, Procedia Computer Science, Vol. 57, pp. 1074-1080. Elsevier B.V., Netherland, 2015
[21] M.Anusha and J.G.R.Sathiaseelan, “An Enhanced K-means Genetic Algorithms for Optimal Clustering”, IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), pp.580-584, 2014.
[22] Zihayat, Morteza, and Aijun, “Mining top-k high utility patterns over data streams”, Inf. Sci.Vol. 285, No.20, pp. 138–161, 2014.
[23] Ryang, Heungmo, Yun, Unil, “Top-k high utility pattern mining with effective threshold raising strategies”, Knowl.-Based Syst. Vol.76, pp.109–126. 2015.
[24] Yang, Yi, Yan, Da, Wu, Huanhuan, Cheng, James, Zhou, Shuigeng, Lui, JohnC.S., “Diversified temporal subgraph pattern mining”, Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1965–1974. 2016.
[25] Tseng, Vincent S., Wu, Cheng-Wei, Shie, Bai-En, Yu, Philip S., “UP-growth: an efficient algorithm for high utility item set mining”, Proceedings of ACM SIGKDD Conference on Knowledge Discovery and Data Mining. ACM, pp. 253–262, 2010.
[26] Kannimuthu, S., Premalatha, K., “Discovery of high utility itemsets using genetic algorithm with ranked mutation” Appl. Artif. Intell, Vol. 28, No.4, pp. 337–359, 2014.
[27] Lin, Jerry Chun-Wei, Yang, Lu, Fournier-Viger, Philippe, Hong, Tzung-Pei, Voznak, Miroslav, “A binary PSO approach to mine high-utility itemsets”, Soft Comput. Vol. 21, pp. 5103–5121. 2017.
[28] Wu, Jimmy Mingtai, Zhan, Justin, Lin, Jerry Chunwei, “An ACO-based approach to mine high-utility itemsets”, Knowl.-Based Syst, 2017. Vol.116, pp.102–113. 2017.
[29] M.Anusha and J.G.R. Sathiaseelan, “Evolutionary Clustering Algorithm using Criterion-Knowledge-Ranking for Multi-objective Optimization”, Wireless Personal Communication, Springer, Vol.94, pp.2009-2030, Springer, USA. 2017
[30] M.Anusha and J.G.R. Sathiaseelan, “An Improved K-Means Genetic Algorithm for Multi-objective Optimization”, International Journal of Applied Engineering Research, pp. 228-231, 2015.