Automatic Data Clustering based on Hybrid Atom Search Optimization and Sine-Cosine Algorithm

Automatic clustering based hybrid metaheuristic algorithms has attracted the center of interest of scientists and engineers which become a hot topic for different data analysis applications. For example, image clustering, bioinformatics, image segmentation, and natural language processing. Where the process of determining the number and position of centroids is an NP-hard problem. So, this paper presents an alternative automatic clustering algorithm based on the hybrid between the atom search optimization (ASO) and the sine-cosine algorithm (SCA). The main objective of the proposed clustering method, called ASOSCA, is to find automatically the optimal number of centroids and their positions in order to minimize the CS-index (which refers to Compact-separated index). To achieve this goal, the ASOSCA uses SCA as a local search operator to improve the quality of ASO. The performance of the proposed hybrid method is compared with other metaheuristic methods; in which all of them are tested on sixteen clustering datasets and using different cluster validity indexes as Dunn, Silihouette, Davies Bouldin, and Calinski Harabasz. The experimental results show that the ASOSCA depict high superiority in comparison with other types of hybrid metaheuristic in terms of clustering measures.

[1]  Mohammad-Reza Feizi-Derakhshi,et al.  Fuzzy clustering based on Forest optimization algorithm , 2018, J. King Saud Univ. Comput. Inf. Sci..

[2]  Pengfei Duan,et al.  A Hybrid Method of Sine Cosine Algorithm and Differential Evolution for Feature Selection , 2017, ICONIP.

[3]  Dervis Karaboga,et al.  A comparative study of Artificial Bee Colony algorithm , 2009, Appl. Math. Comput..

[4]  Zhenxing Zhang,et al.  Atom search optimization and its application to solve a hydrogeologic parameter estimation problem , 2019, Knowl. Based Syst..

[5]  Yongquan Zhou,et al.  Automatic data clustering using nature-inspired symbiotic organism search algorithm , 2019, Knowl. Based Syst..

[6]  M.-C. Su,et al.  A new cluster validity measure and its application to image compression , 2004, Pattern Analysis and Applications.

[7]  Aboul Ella Hassanien,et al.  Chaotic multi-verse optimizer-based feature selection , 2017, Neural Computing and Applications.

[8]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[9]  Gadadhar Sahoo,et al.  A two-step artificial bee colony algorithm for clustering , 2017, Neural Computing and Applications.

[10]  Guliz Toz,et al.  A fuzzy image clustering method based on an improved backtracking search optimization algorithm with an inertia weight parameter , 2019, J. King Saud Univ. Comput. Inf. Sci..

[11]  Ramachandra Rao Kurada,et al.  Automatic Teaching–Learning-Based Optimization: A Novel Clustering Method for Gene Functional Enrichments , 2015 .

[12]  Zhenxing Zhang,et al.  A novel atom search optimization for dispersion coefficient estimation in groundwater , 2019, Future Gener. Comput. Syst..

[13]  Hojjat Emami,et al.  Integrating Fuzzy K-Means, Particle Swarm Optimization, and Imperialist Competitive Algorithm for Data Clustering , 2015 .

[14]  S. Shanthi,et al.  Data clustering using K-Means based on Crow Search Algorithm , 2018, Sādhanā.

[15]  Dharmender Kumar,et al.  A novel hybrid K-means and artificial bee colony algorithm approach for data clustering , 2018 .

[16]  Mohammed A. A. Al-qaness,et al.  Oil Consumption Forecasting Using Optimized Adaptive Neuro-Fuzzy Inference System Based on Sine Cosine Algorithm , 2018, IEEE Access.

[17]  Ahmed A. Ewees,et al.  Improved grasshopper optimization algorithm using opposition-based learning , 2018, Expert Syst. Appl..

[18]  J. Sasikala,et al.  Adaptive chemical reaction based spatial fuzzy clustering for level set segmentation of medical images , 2016, Ain Shams Engineering Journal.

[19]  Yongquan Zhou,et al.  A simplex method-based social spider optimization algorithm for clustering analysis , 2017, Eng. Appl. Artif. Intell..

[20]  Seyedali Mirjalili,et al.  SCA: A Sine Cosine Algorithm for solving optimization problems , 2016, Knowl. Based Syst..

[21]  Rui Xu,et al.  Survey of clustering algorithms , 2005, IEEE Transactions on Neural Networks.

[22]  Abdolreza Hatamlou,et al.  An efficient hybrid clustering method based on improved cuckoo optimization and modified particle swarm optimization algorithms , 2018, Appl. Soft Comput..

[23]  Chin-Teng Lin,et al.  A review of clustering techniques and developments , 2017, Neurocomputing.

[24]  Diego Oliva,et al.  Context based image segmentation using antlion optimization and sine cosine algorithm , 2018, Multimedia Tools and Applications.

[25]  Victor Ströele,et al.  An Ant Colony Optimization for Automatic Data Clustering Problem , 2018, 2018 IEEE Congress on Evolutionary Computation (CEC).

[26]  Satyasai Jagannath Nanda,et al.  A Grey Wolf Optimizer Based Automatic Clustering Algorithm for Satellite Image Segmentation , 2017 .

[27]  Ibrahim Berkan Aydilek A hybrid firefly and particle swarm optimization algorithm for computationally expensive numerical problems , 2018, Appl. Soft Comput..

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

[29]  Essam Said Hanandeh,et al.  A novel hybridization strategy for krill herd algorithm applied to clustering techniques , 2017, Appl. Soft Comput..

[30]  V. J. Rayward-Smith,et al.  Fuzzy Cluster Analysis: Methods for Classification, Data Analysis and Image Recognition , 1999 .

[31]  Saeed Jalili,et al.  Dynamic clustering using combinatorial particle swarm optimization , 2012, Applied Intelligence.

[32]  Satish Kumar Injeti,et al.  Optimal multilevel thresholding selection for brain MRI image segmentation based on adaptive wind driven optimization , 2018, Measurement.

[33]  A. N. Jadhav,et al.  WGC: Hybridization of exponential grey wolf optimizer with whale optimization for data clustering , 2017, Alexandria Engineering Journal.

[34]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[35]  Babak Nasiri,et al.  A Novel History-driven Artificial Bee Colony Algorithm for Data Clustering , 2018, Appl. Soft Comput..

[36]  Dan Boneh,et al.  On genetic algorithms , 1995, COLT '95.

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

[38]  Zahid Halim,et al.  Optimizing the minimum spanning tree-based extracted clusters using evolution strategy , 2017, Cluster Computing.

[39]  Sriparna Saha,et al.  Exploring differential evolution and particle swarm optimization to develop some symmetry-based automatic clustering techniques: application to gene clustering , 2018, Neural Computing and Applications.

[40]  R. J. Kuo,et al.  Automatic clustering using an improved artificial bee colony optimization for customer segmentation , 2018, Knowledge and Information Systems.

[41]  Ravi Kumar Jatoth,et al.  Hybrid SCA–TLBO: a novel optimization algorithm for global optimization and visual tracking , 2019, Neural Computing and Applications.