Commenced in January 2007
Paper Count: 30127
Chemical Reaction Algorithm for Expectation Maximization Clustering
Abstract:Clustering is an intensive research for some years because of its multifaceted applications, such as biology, information retrieval, medicine, business and so on. The expectation maximization (EM) is a kind of algorithm framework in clustering methods, one of the ten algorithms of machine learning. Traditionally, optimization of objective function has been the standard approach in EM. Hence, research has investigated the utility of evolutionary computing and related techniques in the regard. Chemical Reaction Optimization (CRO) is a recently established method. So the property embedded in CRO is used to solve optimization problems. This paper presents an algorithm framework (EM-CRO) with modified CRO operators based on EM cluster problems. The hybrid algorithm is mainly to solve the problem of initial value sensitivity of the objective function optimization clustering algorithm. Our experiments mainly take the EM classic algorithm:k-means and fuzzy k-means as an example, through the CRO algorithm to optimize its initial value, get K-means-CRO and FKM-CRO algorithm. The experimental results of them show that there is improved efficiency for solving objective function optimization clustering problems.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1127603Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 874
 Albert Y. S. Lam and Victor O. K. Li, Chemical reaction optimization: a tutorial, Memetic Computing, Vol. 4, No. 1, 2013.
 Sanghamitra Bandyopadhyay, Genetic algorithms for clustering and fuzzy clustering, WIREs Data Mining and Knowledge Discovery, Volume 1, December 2011.
 Siripen Wikaisuksakul, A multi-objective genetic algorithm with fuzzy c-means for automatic data clustering, Applied Soft Computing, Vol. 24, 2014.
 David Hallac Jure Leskovec, Stephen Boyd, Network Lasso: Clustering and Optimization in Large Graphs, KDD15, August 2015.
 Grgoire A. Gallet and Fabio Pietrucci, Structural cluster analysis of chemical reactions in solution, The Journal of Chemical Physics, Vol 139, 2013.
 P. C. Srinivasa Rao1.Haider Banka1, Novel chemical reaction optimization based unequal clustering and routing algorithms for wireless sensor networks, Wireless Netw, Vol 12, January 2016.
 Yoon Mi Hamrick and Michael D. Morse, Comparative Cluster Reaction Studies of the V, Nb, and T a Series, The Journal of Physical Chemistry, Vol. 93, No. 17, 1989.
 M.-S. YANG, A Survey of Fuzzy Clustering, Mathl. Comput Modelling, Vol. 18, No. 11, 1993.
 Chenglong Tang Shigang Wang, Wei Xu, New fuzzy c-means clustering model based on the data weighted approach, Data&Knowledge Engineering, Vol. 69, 2010.
 Dan Li Hong Gu Liyong Zhang, A fuzzy c-means clustering algorithm based on nearest-neighbor intervals for incomplete data, Expert Systems with Applications, Vol 37, 2010.
 Matteo Brunelli Mario Fedrizzi Michele Fedrizzi, Fuzzy m-ary adjacency relations in social network analysis, Information Fusion, Vol 17, 2014.
 Du-Ming Tsai Chung-Chan Lin, Fuzzy C-means based clustering for linearly and nonlinearly separable data, Pattern Recognition, Vol 44, 2011.
 Lun Hu and Keith C. C. Chan, Fuzzy Clustering in a Complex Network Based on Content Relevance and Link Structures, IEEE Transactions on Fuzzy Systems, vol. 24, No 2, 2016.
 Burhan Tu rksen, Review of fuzzy system models with an emphasis on fuzzy functions, Transactions of the Institute of Measurement and Control , Vol 31, 2009.
 Isabel Timn, Jess Soto, Horacio Prez-Snchez, Jos M. Cecilia, Parallel implementation of fuzzy minimals clustering algorithm, Expert Systems With Applications, Vol 41, 2016
 Tien Trong Nguyen, ZhiYong Li, ShiWen Zhang, Tung Khac Truong, A hybrid algorithm based on particle swarm and chemical reaction, Expert Systems with Applications 41 (2014).
 Liang Bai, Jiye Liang, Cluster validity functions for categorical data: a solution-space perspective, Data Min Knowl Disc, 2014
 Raghu Krishnapuram,and James M. Keller, A Possibilistic Approach to Clustering, IEEE Transactions on Fuzzy Systems, vol. 1, no. 2, May 1993
 Dempster, A P, Laird, N M, Rubin, D B. Maximum Likelihood for Incomplete Data via the EM Algorithm (J). 1977.
 Hartigan J A, Wong M A. A K-means clustering algorithm.(J). Applied Statistics, 2013, 28(1):100-108.