Memory-enriched big bang–big crunch optimization algorithm for data clustering

Cluster analysis plays an important role in decision-making process for many knowledge-based systems. There exist a wide variety of different approaches for clustering applications including the heuristic techniques, probabilistic models, and traditional hierarchical algorithms. In this paper, a novel heuristic approach based on big bang–big crunch algorithm is proposed for clustering problems. The proposed method not only takes advantage of heuristic nature to alleviate typical clustering algorithms such as k-means, but it also benefits from the memory-based scheme as compared to its similar heuristic techniques. Furthermore, the performance of the proposed algorithm is investigated based on several benchmark test functions as well as on the well-known datasets. The experimental results show the significant superiority of the proposed method over the similar algorithms.

[1]  Erwie Zahara,et al.  A hybridized approach to data clustering , 2008, Expert Syst. Appl..

[2]  Liviu P. Dinu,et al.  Clustering based on median and closest string via rank distance with applications on DNA , 2013, Neural Computing and Applications.

[3]  A. Rezaee Jordehi.  A chaotic artificial immune system optimisation algorithm for solving global continuous optimisation problems , 2014, Neural Computing and Applications.

[4]  A. Rezaee Jordehi.  A chaotic-based big bang–big crunch algorithm for solving global optimisation problems , 2014 .

[5]  A. Rezaee Jordehi,et al.  Enhanced leader PSO (ELPSO): A new algorithm for allocating distributed TCSC's in power systems , 2015 .

[6]  V. M. Istemihan Genc,et al.  Preventive and corrective control applications in power systems via Big Bang–Big Crunch optimization , 2015 .

[7]  Dantong Ouyang,et al.  An artificial bee colony approach for clustering , 2010, Expert Syst. Appl..

[8]  Khaled S. Al-Sultan,et al.  A Tabu search approach to the clustering problem , 1995, Pattern Recognit..

[9]  Kevin P. Murphy,et al.  Machine learning - a probabilistic perspective , 2012, Adaptive computation and machine learning series.

[10]  A. Rezaee Jordehi,et al.  A chaotic-based big bang–big crunch algorithm for solving global optimisation problems , 2014, Neural Computing and Applications.

[11]  A. Rezaee Jordehi,et al.  Enhanced leader PSO (ELPSO): A new PSO variant for solving global optimisation problems , 2015, Appl. Soft Comput..

[12]  W. Welch.  Algorithmic complexity: three NP- hard problems in computational statistics , 1982 .

[13]  Andrew Chi-Sing Leung,et al.  PSO-based K-Means clustering with enhanced cluster matching for gene expression data , 2012, Neural Computing and Applications.

[14]  B. Kulkarni,et al.  An ant colony approach for clustering , 2004 .

[15]  Xin-Ping Guan,et al.  A new particle swarm optimization algorithm with adaptive inertia weight based on Bayesian techniques , 2015, Appl. Soft Comput..

[16]  Nasser M. Nasrabadi,et al.  Pattern Recognition and Machine Learning , 2006, Technometrics.

[17]  A. Rezaee Jordehi,et al.  Brainstorm optimisation algorithm (BSOA): An efficient algorithm for finding optimal location and setting of FACTS devices in electric power systems , 2015 .

[18]  Anil K. Jain.  Data clustering: 50 years beyond K-means , 2008, Pattern Recognit. Lett..

[19]  M. Fesanghary,et al.  An improved harmony search algorithm for solving optimization problems , 2007, Appl. Math. Comput..

[20]  A. Rezaee Jordehi,et al.  An efficient chaotic water cycle algorithm for optimization tasks , 2015, Neural Computing and Applications.

[21]  R. Eberhart,et al.  Empirical study of particle swarm optimization , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[22]  Tunchan Cura,et al.  A particle swarm optimization approach to clustering , 2012, Expert Syst. Appl..

[23]  Rowena Chau,et al.  Cluster identification and separation in the growing self-organizing map: application in protein sequence classification , 2009, Neural Computing and Applications.

[24]  Ganapati Panda,et al.  A survey on nature inspired metaheuristic algorithms for partitional clustering , 2014, Swarm Evol. Comput..

[25]  Songtao Xue,et al.  Big Bang-Big Crunch optimization for parameter estimation in structural systems , 2010 .

[26]  Parham Moradi,et al.  A hybrid particle swarm optimization for feature subset selection by integrating a novel local search strategy , 2016, Appl. Soft Comput..

[27]  Andrew Lewis,et al.  Grey Wolf Optimizer , 2014, Adv. Eng. Softw..

[28]  Jianhong Wu,et al.  Data clustering - theory, algorithms, and applications , 2007 .

[29]  A. Rezaee Jordehi.  Seeker optimisation (human group optimisation) algorithm with chaos , 2015, J. Exp. Theor. Artif. Intell..

[30]  H. K. Verma,et al.  TLBO based Voltage Stable Environment Friendly Economic Dispatch Considering Real and Reactive Power Constraints , 2013 .

[31]  M. Narasimha Murty,et al.  Genetic K-means algorithm , 1999, IEEE Trans. Syst. Man Cybern. Part B.

[32]  Anil K. Jain.  Data clustering: 50 years beyond K-means , 2010, Pattern Recognit. Lett..

[33]  Ibrahim Eksin,et al.  A new optimization method: Big Bang-Big Crunch , 2006, Adv. Eng. Softw..

[34]  Aboul Ella Hassanien,et al.  Adaptive k-means clustering algorithm for MR breast image segmentation , 2013, Neural Computing and Applications.

[35]  Parham Moradi,et al.  An unsupervised feature selection algorithm based on ant colony optimization , 2014, Eng. Appl. Artif. Intell..

[36]  Bo Xing,et al.  Innovative Computational Intelligence: A Rough Guide to 134 Clever Algorithms , 2013 .