Application of Charge System Search Algorithm for Data Clustering

This chapter presents a charged system search (CSS) optimization method for finding the optimal cluster centers for a given dataset. In CSS algorithm, while the Coulomb and Gauss laws from electrostatics are applied to initiate the local search, global search is performed using Newton second law of motion from mechanics. The efficiency and capability of the proposed algorithm is tested on seven datasets and compared with existing algorithms like K-Means, GA, PSO and ACO. From the experimental results, it is found that the proposed algorithm provides more accurate and effective results in comparison to other existing algorithms.

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

[2]  Pavel Berkhin,et al.  A Survey of Clustering Data Mining Techniques , 2006, Grouping Multidimensional Data.

[3]  Angelo Dalli.  Adaptation of the F-measure to Cluster Based Lexicon Quality Evaluation , 2003 .

[4]  Pekka Teppola,et al.  Adaptive Fuzzy C-Means clustering in process monitoring , 1999 .

[5]  Gadadhar Sahoo,et al.  A Chaotic Charged System Search Approach for Data Clustering , 2014, Informatica.

[6]  Salwani Abdullah,et al.  A combined approach for clustering based on K-means and gravitational search algorithms , 2012, Swarm Evol. Comput..

[7]  Salwani Abdullah,et al.  Data Clustering Using Big Bang–Big Crunch Algorithm , 2011 .

[8]  Taher Niknam,et al.  An efficient hybrid evolutionary optimization algorithm based on PSO and SA for clustering , 2009 .

[9]  Xiaohua Hu,et al.  Towards effective document clustering: A constrained K-means based approach , 2008, Inf. Process. Manag..

[10]  Chang Sup Sung,et al.  A tabu-search-based heuristic for clustering , 2000, Pattern Recognit..

[11]  E. Forgy,et al.  Cluster analysis of multivariate data : efficiency versus interpretability of classifications , 1965 .

[12]  Russell C. Eberhart,et al.  A discrete binary version of the particle swarm algorithm , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[13]  W. Pan,et al.  Cluster analysis using multivariate normal mixture models to detect differential gene expression with microarray data , 2006, Comput. Stat. Data Anal..

[14]  Cheng-Fa Tsai,et al.  ACODF: a novel data clustering approach for data mining in large databases , 2004 .

[15]  Shokri Z. Selim,et al.  A simulated annealing algorithm for the clustering problem , 1991, Pattern Recognit..

[16]  J. Bezdek.  Numerical taxonomy with fuzzy sets , 1974 .

[17]  Gadadhar Sahoo,et al.  A charged system search approach for data clustering , 2014, Progress in Artificial Intelligence.

[18]  Salwani Abdullah,et al.  Application of Gravitational Search Algorithm on Data Clustering , 2011, RSKT.

[19]  James C. Bezdek,et al.  On cluster validity for the fuzzy c-means model , 1995, IEEE Trans. Fuzzy Syst..

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

[21]  Witold Pedrycz,et al.  The fuzzy C-means algorithm with fuzzy P-mode prototypes for clustering objects having mixed features , 2009, Fuzzy Sets Syst..

[22]  T Watson Layne,et al.  A Genetic Algorithm Approach to Cluster Analysis , 1998 .

[23]  Hong Zhou,et al.  Accurate integration of multi-view range images using k-means clustering , 2008, Pattern Recognit..

[24]  M Dorigo,et al.  Ant colonies for the travelling salesman problem. , 1997, Bio Systems.

[25]  Anil K. Jain,et al.  Data clustering: a review , 1999, CSUR.

[26]  Enrique H. Ruspini,et al.  A New Approach to Clustering , 1969, Inf. Control..

[27]  Dervis Karaboga,et al.  A novel clustering approach: Artificial Bee Colony (ABC) algorithm , 2011, Appl. Soft Comput..

[28]  A. Kaveh,et al.  A novel heuristic optimization method: charged system search , 2010 .

[29]  Gadadhar Sahoo,et al.  An Improved Cat Swarm Optimization Algorithm for Clustering , 2015 .

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

[31]  Ali Maroosi,et al.  Application of honey-bee mating optimization algorithm on clustering , 2007, Appl. Math. Comput..

[32]  Thrasyvoulos N. Pappas.  An adaptive clustering algorithm for image segmentation , 1992, IEEE Trans. Signal Process..

[33]  Yugal Kumar,et al.  Modified Teacher Learning Based Optimization Method for Data Clustering , 2014, SIRS.

[34]  Taher Niknam,et al.  An efficient hybrid approach based on PSO, ACO and k-means for cluster analysis , 2010, Appl. Soft Comput..

[35]  C. A. Murthy,et al.  In search of optimal clusters using genetic algorithms , 1996, Pattern Recognit. Lett..

[36]  Ujjwal Maulik,et al.  Genetic algorithm-based clustering technique , 2000, Pattern Recognit..

[37]  W J Dunn,et al.  Use of cluster analysis in the development of structure-activity relations for antitumor triazenes. , 1976, Journal of medicinal chemistry.

[38]  Witold Pedrycz,et al.  Kernel-based fuzzy clustering and fuzzy clustering: A comparative experimental study , 2010, Fuzzy Sets Syst..

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

[40]  Khaled S. Al-Sultan,et al.  Computational experience on four algorithms for the hard clustering problem , 1996, Pattern Recognit. Lett..