Abstract
In this paper, we propose a Memetic-based clustering method that improves the partitioning of document clustering. Our proposed method is named as Differential Evolution Memetic Clustering (DEMC). Differential Evolution (DE) is used for the selection of the best set of cluster centres (centroids) while the Chaotic Logistic Search (CLS) is used to enhance the best set of solutions found by DE. For the purpose of comparison, the DEMC is compared with the basic DE, Differential Evolution Simulated Annealing (DESA) and the Differential Evolution K-Means (DEKM) methods as well as the traditional partitioning clustering using the K-means. The DEMC is also compared with the recently proposed Chaotic Gradient Artificial Bee Colony (CGABC) document clustering method. The reuters-21578, a pair of the 20-news group, classic 3 and TDT benchmark collection (TDT5) along with real-world six-event-crimes datasets are used in the experiments in this paper. The results showed that the proposed DEMC outperformed the other methods in terms of the convergence rate measured by the fitness function (ADDC) and the compactness of the resulted clusters measured by the F-macro and F-micro measures.
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References
Song, W., Yingying, Q., Soon Cheol, P., Xuezhong, Q.: A hybrid evolutionary computation approach with its application for optimizing text document clustering. Expert Syst. Appl. 42(5), 2517–2524 (2015)
Liu, G., Yuanxiang, L., Xin, N., Hao, Z.: A novel clustering-based differential evolution with 2 multi-parent crossovers for global optimization. Appl. Soft Comput. 12(2), 663–681 (2012)
Nanda, S.J., Panda, G.: A survey on nature inspired metaheuristic algorithms for partitional clustering. Swarm Evol. Comput. 16, 1–18 (2014)
Kramer, O., Ciaurri, D.E., Koziel, S.: Derivative-free optimization. In: Koziel, S., Yang, X.S. (eds.) Computational Optimization, Methods and Algorithms, vol. 356, pp. 61–83. Springer, Heidelberg (2011). doi:10.1007/978-3-642-20859-1_4
Li, B., Jiang, W.: Chaos optimization method and its application. Control Theory Appl. 4, 028 (1997)
Ong, Y.-S., Meng-Hiot, L., Ning, Z., Kok-Wai, W.: Classification of adaptive memetic algorithms: a comparative study. IEEE Trans. Syst. Man Cybern. Part B Cybern. 36(1), 141–152 (2006)
Moscato, P.: On evolution, search, optimization, genetic algorithms and martial arts: towards memetic algorithms. Caltech concurrent computation program, C3P Report, 1989. 826 (1989)
Cobos, C., Claudia, M., María-Fernanda, M., Martha, M., Elizabeth, L.: Web document clustering based on a new niching memetic algorithm, term-document matrix and Bayesian information criterion. In: IEEE Congress on Evolutionary Computation (CEC) Evolutionary 2010, pp. 1–8. IEEE, Barcelona, Spain (2010)
Celebi, M.E. (ed.): Partitional Clustering Algorithms, 1st edn. Springer, Cham (2015). doi:10.1007/978-3-319-09259-1
Cobos, C., Martha, M., Errol, L., Milos, M., Enrique, H.: Clustering of web search results based on an Iterative Fuzzy C-means Algorithm and Bayesian Information Criterion. In: IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS) 2013. IEEE, Edmonton (2013)
Abraham, A., Das, S., Konar, A.: Document clustering using differential evolution. in Evolutionary Computation. In IEEE Congress on Evolutionary Computation (CEC) 2006. IEEE, Sheraton Vancouver Wall Center Vancouver, BC, Canada (2006)
Peng, L., Yanyun, Z., Guangming, D., Maocai, W.: Memetic differential evolution with an improved contraction criterion. Comput. Intell. Neurosci. (2017)
Reynoso-Meza, G., Javier, S., Xavier, B., Juan, H.: Hybrid DE algorithm with adaptive crossover operator for solving real-world numerical optimization problems. In: IEEE Congress on Evolutionary Computation (CEC) 2011. IEEE, New Orleans (2011)
Poikolainen, I. and F. Neri. Differential evolution with concurrent fitness based local search. In: IEEE Congress on Evolutionary Computation (CEC) 2013. IEEE, Cancun (2013)
Zhang, C., Chen, J., Xin, B.: Distributed memetic differential evolution with the synergy of Lamarckian and Baldwinian learning. Appl. Soft Comput. 13(5), 2947–2959 (2013)
Jia, D., Zheng, G., Khan, M.K.: An effective memetic differential evolution algorithm based on chaotic local search. Inf. Sci. 181(15), 3175–3187 (2011)
Guo, Z., Haixia, H., Changshou, D., Xuezhi, Y., Zhijian, W.: An enhanced differential evolution with elite chaotic local search. Comput. Intell. Neurosci. 2015, 6 (2015)
Chunming, F., Xu, Y., Chao, J., Han, X., Zhiliang, H.: Improved differential evolution with shrinking space technique for constrained optimization. Chin. J. Mech. Eng., 1–13 (2017)
Forsati, R., Mehrdad, M., Mehrnoush, S., Mohammad, R.M.: Efficient stochastic algorithms for document clustering. Inf. Sci. 220, 269–291 (2013)
Bharti, K.K., Singh, P.K.: Chaotic gradient artificial bee colony for text clustering. Soft. Comput. 20(3), 1113–1126 (2016)
Saruhan, H.: Differential evolution and simulated annealing algorithms for mechanical systems design. Int. J. Eng. Sci. Technol. 17(3), 131–136 (2014)
Kwedlo, W.: A: clustering method combining differential evolution with the K-means algorithm. Pattern Recogn. Lett. 32(12), 1613–1621 (2011)
Zhu, R., Aston, Z., Jian, P., Chengxiang, Z.: Exploiting temporal divergence of topic distributions for event detection. In: International Conference on Big Data 2016. IEEE, Washington, DC (2017)
Acknowledgements
Ibraheem would like to express his gratitude to the Higher Committee of Education Development in Iraq (HECD) for the scholarship he has received to fund his PhD study.
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Al-Jadir, I., Wong, K.W., Fung, C.C., Xie, H. (2017). Differential Evolution Memetic Document Clustering Using Chaotic Logistic Local Search. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10634. Springer, Cham. https://doi.org/10.1007/978-3-319-70087-8_23
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