NEWS

RAT SWARM OPTIMIZER FOR DATA CLUSTERING


(Received: 1-Jun.-2022, Revised: 27-Jul.-2022 , Accepted: 18-Aug.-2022)
Rat Swarm Optimizer (RSO) is one of the newest swarm intelligence optimization algorithms that is inspired from the behaviors of chasing and fighting of rats in nature. In this paper, we will apply the RSO to one of the most challenging problems, which is data clustering. The search capability of RSO is used here to find the best cluster centers. The proposed RSO algorithm for clustering (RSOC) is tested on several benchmarks and compared to some other optimization algorithms for data clustering, including some well- known and powerful algorithms such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), as well as other recent algorithms, such as the Hybridization of Krill Herd Algorithm and harmony search (H-KHA), hybrid Harris Hawks Optimization with differential evolution (H-HHO) and Multi-Verse Optimizer (MVO). Results are validated through a bunch of measures: homogeneity, completeness, v-measure, purity and error rate. The computational results are encouraging, where they demonstrate the effectiveness of RSOC over other clustering techniques.

[1] J. Han, M. Kamber and J. Pei, Data Mining: Concepts and Techniques, 3rd Edn., San Francisco, CA, USA: Morgan Kaufmann Publishers Inc., 2011.

[2] A. M. Bagirov, N. Karmitsa and S. Taheri, Partitional Clustering via Nonsmooth Optimization: Clustering via Optimization, Springer Nature, 2020.

[3] P. Berkhin, "A Survey of Clustering Data Mining Techniques," Proc. of Grouping Multidimensional Data, pp. 25–71, Springer, 2006.

[4] B. Everitt, S. Landau, M. Leese and D. Stahl, Cluster Analysis, ser. Wiley Series in Probability and Statistics, Wiley, [Online], Available: https://books.google.dz/books?id=WSayDAEACAAJ, 2011.

[5] J. Hartigan, Clustering Algorithms, John Wiley and Sons, New York, 1975.

[6] K. Krippendorff, "Clustering," Book Chapter, Multivariate Techniques in Human Communication Research, pp. 259-308, Elsevier, 1980.

[7] K. Bailey, "Cluster Analysis," Book Chapter, Sociological Methodology, pp. 59-128, DOI: 10.2307/270894, 1975.

[8] A. K. Jain and R. C. Dubes, Algorithms for Clustering Data, USA: Prentice-Hall, Inc., 1988.

[9] B. Mirkin, Clustering for Data Mining: A Data Recovery Approach, DOI:10.1201/9781420034912, 2005.

[10] R. Xu and D. Wunsch, Clustering, vol. 10, John Wiley & Sons, 2008.

[11] A. Naik and S. C. Satapathy, "Past Present Future: A New Human-based Algorithm for Stochastic Optimization," Soft Computing, vol. 25, no. 20, pp. 12 915–12 976, 2021.

[12] M. Dorigo, V. Maniezzo and A. Colorni, "Ant System: Optimization by a Colony of Cooperating Agents," IEEE Trans. on Systems, Man and Cybernetics, Part B (Cybernetics), vol. 26, no. 1, pp. 29–41, 1996.

[13] J. Kennedy and R. Eberhart, "Particle Swarm Optimization," Proc. of the International Conference on Neural Networks (ICNN’95), vol. 4, , pp. 1942–1948, 1995.

[14] S. Z. Selim and K. Alsultan, "A Simulated Annealing Algorithm for the Clustering Problem," Pattern Recognition, vol. 24, no. 10, pp. 1003–1008, 1991.

[15] K. S. Al-Sultan, "A Tabu Search Approach to the Clustering Problem," Pattern Recognition, vol. 28, no. 9, pp. 1443–1451, 1995.

[16] F. Glover, "Future Paths for Integer Programming and Links to Artificial Intelligence," Computers & Operations Research, vol. 13, no. 5, pp. 533–549, 1986.

[17] F. Glover, "Tabu Search—Part i," ORSA Journal on Computing, vol. 1, no. 3, pp. 190–206, 1989.

[18] D. E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, 1st Edn., USA: Addison-Wesley Longman Publishing Co., Inc., 1989.

[19] J. H. Holland, Adaptation in Natural and Artificial Systems, Ann Arbor, MI: University of Michigan Press, 1975, 2nd Edition, 1992.

[20] M. C. Cowgill, R. J. Harvey and L. T. Watson, "A Genetic Algorithm Approach to Cluster Analysis," Computers & Mathematics with Applications, vol. 37, no. 7, pp. 99–108, 1999.

[21] E. Falkenauer, Genetic Algorithms and Grouping Problems, John Wiley & Sons, Inc., 1998.

[22] E. R. Hruschka, R. J. Campello, A. A. Freitas et al., "A Survey of Evolutionary Algorithms for Clustering," IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews), vol. 39, no. 2, pp. 133–155, 2009.

[23] P. Shelokar, V. K. Jayaraman and B. D. Kulkarni, "An Ant Colony Approach for Clustering," Analytica Chimica Acta, vol. 509, no. 2, pp. 187–195, 2004.

[24] J. Ji, W. Pang, Y. Zheng, Z. Wang and Z. Ma, "A Novel Artificial Bee Colony Based Clustering Algorithm for Categorical Data," PloS One, vol. 10, no. 5, p. e0127125, 2015.

[25] D. Karaboga and B. Basturk, "An Artificial Bee Colony (ABC) Algorithm for Numeric Function Optimization," Proc. of the IEEE Swarm Intelligence Symposium, pp. 181–184, Indianapolis, USA, 2006.

[26] D. Van der Merwe and A. P. Engelbrecht, "Data Clustering Using Particle Swarm Optimization," Proc. of the IEEE Congress on Evolutionary Computation (CEC’03), vol. 1, pp. 215–220, 2003.

[27] Y.-T. Kao, E. Zahara and I.-W. Kao, "A Hybridized Approach to Data Clustering," Expert Systems with Applications, vol. 34, no. 3, pp. 1754–1762, 2008.

[28] T. Cura, "A Particle Swarm Optimization Approach to Clustering," Expert Systems with Applications, vol. 39, no. 1, pp. 1582–1588, 2012.

[29] X. Zhang, Q. Lin, W. Mau, Z. Dou and G. Liu, "Hybrid Particle Swarm and Grey Wolf Optimizer and Its Application to Clustering Optimization, " Applied Soft Computing, vol. 101, p. 107061, 2021.

[30] S. Mirjalili, S. M. Mirjalili and A. Lewis, "Grey Wolf Optimizer," Advances in Engineering Software, vol. 69, pp. 46–61, 2014.

[31] V. Kumar, J. K. Chhabra and D. Kumar, "Grey Wolf Algorithm-based Clustering Technique," Journal of Intelligent Systems, vol. 26, no. 1, pp. 153–168, 2017.

[32] N. Kushwaha, M. Pant, S. Kant and V. Jain, "Magnetic Optimization Algorithm for Data Clustering," Pattern Recognition Letters, vol. 115, pp. 59-65, [Online], Available: 10.1016/j.patrec.2017.10.031, 2018.

[33] H. A. Abdulwahab, A. Noraziah, A. A. Alsewari and S. Q. Salih, "An Enhanced Version of Black Hole Algorithm via Levy Flight for Optimization and Data Clustering Problems," IEEE Access, vol. 7, pp. 142085-142096, DOI: 10.1109/ACCESS.2019.2937021, 2019.

[34] I. Aljarah, M. Mafarja, A. A. Heidari, H. Faris and S. Mirjalili, "Clustering Analysis Using a Novel Locality-informed Grey Wolf-inspired Clustering Approach," Knowledge and Information Systems, vol. 62, no. 2, pp. 507–539, 2020.

[35] I. Aljarah, M. Mafarja, A. A. Heidari, H. Faris and S. Mirjalili, "Multi-verse Optimizer: Theory, Literature Review and Application in Data Clustering," Nature-inspired Optimizers, vol. 811, pp. 123–141, 2020.

[36] S. Mirjalili, S. M. Mirjalili and A. Hatamlou, "Multi-verse Optimizer: A Nature-inspired Algorithm for Global Optimization," Neural Computing and Applications, vol. 27, no. 2, pp. 495–513, 2016.

[37] G. Dhiman, M. Garg, A. Nagar, V. Kumar and M. Dehghani, "A Novel Algorithm for Global Optimization: Rat Swarm Optimizer," Journal of Ambient Intelligence and Humanized Computing, vol. 12, no. 8, pp. 8457–8482, 2021.

[38] M. Dhas and N. Singh, "Blood Cell Image Denoising Based on Tunicate Rat Swarm Optimization with Median Filter," Evolutionary Computing and Mobile Sustainable Networks, vol. 116, pp. 33–45, 2022.

[39] A. Tamilarasan, A. Renugambal and V. Dharanendran, "Parametric Estimation for AWJ Cutting of TI-6AL-4V Alloy Using Rat Swarm Optimization Algorithm," Materials and Manufacturing Processes, pp. 1–11, DOI: 10.1080/10426914.2022.2065011, 2022.

[40] M. Eslami, E. Akbari, S. T. Seyed Sadr and B. Ibrahim, "A Novel Hybrid Algorithm Based on Rat Swarm Optimization and Pattern Search for Parameter Extraction of Solar Photovoltaic Models," Energy Science and Engineering, DOI: 10.1002/ese3.1160, 2022.

[41] R. Ghadge and S. Prakash, "Investigation and Prediction of Hybrid Composite Leaf Spring Using Deep Neural Network Based Rat Swarm Optimization," Mechanics Based Design of Structures and Machines, pp. 1–30, DOI: 10.1080/15397734.2021.1972309, 2021.

[42] A. Vasantharaj, P. Rani, S. Huque, K. Raghuram, R. Ganeshkumar and S. Shafi, "Automated Brain Imaging Diagnosis and Classification Model Using Rat Swarm Optimization with Deep Learning Based Capsule Network," International Journal of Image and Graphics, p. 2240001, DOI: 10.1142/S0219467822400010, 2021.

[43] G. Gan, C. Ma and J. Wu, Data Clustering: Theory, Algorithms and Applications, SIAM, 2020.

[44] M. Halkidi, Y. Batistakis and M. Varzigiannis, "Cluster Validity Methods: Part I," ACM Sigmod Record, vol. 31, no. 2, pp. 40-45, 2002.

[45] M. Halkidi, Y. Batistakis and M. Vazirgiannis, "Clustering Validity Checking Methods: Part II,"ACM Sigmod Record, vol. 31, no. 3, pp. 19–27, 2002.

[46] J. Oyelade et al., "Data Clustering: Algorithms and Its Applications," Proc. of the 19th IEEE Int. Conf. on Computational Science and Its Applications (ICCSA), pp. 71–81, St. Petersburg, Russia, 2019.

[47] R. Zafarani, M. A. Abbasi and H. Liu, Social Media Mining: An Introduction, Cambridge University Press, ISBN-10: 1107018854, 2014.

[48] J.-O. Palacio-Niño and F. Berzal, "Evaluation Metrics for Unsupervised Learning Algorithms," arXiv preprint arXiv:1905.05667, 2019.

[49] D. Dua and C. Graff, "UCI Machine Learning Repository," [Online], Available: http://archive.ics.uci.edu/ml, 2017.

[50] L. Abualigah, A. Khader, E. Hanandeh and A. Gandomi, "A Novel Hybridization Strategy for Krill Herd Algorithm Applied to Clustering Techniques", Applied Soft Computing, vol. 60, pp. 423-435, 2017.

[51] L. Abualigah et al., "Hybrid Harris Hawks Optimization with Differential Evolution for Data Clustering," Metaheuristics in Machine Learning: Theory and Applications, vol. 967, pp. 267-299, 2021.

[52] D. Arthur and V. Sergei, "K-Means++: The Advantages of Careful Seeding," Proc. of the 18th Annual ACM-SIAM Symp. on Discrete Algorithms (SODA '07), pp. 1027-1035, 2007.

[53] I. Davidson and S.S. Ravi, "Agglomerative Hierarchical Clustering with Constraints: Theoretical and Empirical Results," Knowledge Discovery in Databases: PKDD 2005, pp. 59-70, [Online], Available: 10.1007/11564126_11, 2005.

[54] U. Maulik and S. Bandyopadhyay, "Genetic Algorithm-based Clustering Technique," Pattern Recognition, vol. 33, no. 9, DOI: 10.1016/S0031-3203(99)00137-5, 2000.

[55] S. Rana, S. Jasola and R. Kumar, "A Review on Particle Swarm Optimization Algorithms and Their Applications to Data Clustering," Artificial Intelligence Review, vol. 35, no. 3, pp. 211-222, 2010.

[56] O. Alia, M. Al-Betar, R. Mandava and A. Khader, "Data Clustering Using Harmony Search Algorithm," Proc. of Int. Conf. on Swarm, Evolutionary and Memetic Computing (SEMCCO 2011), pp. 79-88, DOI: 10.1007/978-3- 642-27242-4_10, 2011.

[57] L. Abualigah, A. Tajudin Khader, M. Azmi AlBetar and E. Said Hanandeh, "A New Hybridization Strategy for Krill Herd Algorithm and Harmony Search Algorithm Applied to Improve the Data Clustering," Proc. of the 1st EAI Int. Conf. on Computer Science and Engineering, DOI: 10.4108/eai.27-2-2017.152255, 2017.