Improve Hybrid Particle Swarm Optimization and K-Means for Clustering

Author

Yudha Alif Auliya, Wayan Firdaus Mahmudy, Sudarto Sudarto

Abstract

Abstract. Potato production is strongly influenced by the selection of suitable land for crops. Criteria for land suitability of planting potatoes is influenced by climatic factors and land characteristics. planted area clustered based on 11 criteria land suitability. The clustering results in the form of four clusters, namely: very suitable (S1), appropriate (S2), is quite suitable (S3) and are not suitable (N). Clustering of land aims to improve the quality and quantity of the potato crop. Clustering is done using a hybrid Particle Swarm Optimization with K-Means (KCPSO). The hybrid method is used to obtain an accurate result cluster. In this study used a new approach to doing improve KCPSO with random injection method. The calculation of the value of cost based on the silhouette coefficient. The results obtained KCPSO showed better results when compared to using the K-Means algorithm without hybrid. The calculation result KCPSO get the best centroid indicated by the value of the largest Silhouette coefficient.

Keywords: Clustering, K-Means, Particle Swarm Optimization, random injection, silhouette Coefficient.

Full Text:

PDF

References


M.F. Barcia, S.N. Muin and N. C. Deta, " Land Suitability correlation with temperature reference Suitability Red Potato Planting in Plain Medium Bengkulu ", jur. Agroekotek, vol. 2, no. 1, pp.21-26,2010

M. E. Djoemaijah, D. Dwiastuti dan D.Setyorini, " Assembled Test Potato Cultivation Technology Specific Location Highlands", J. Pengkajian dan Pengembangan Teknologi Pertanian. vol.2, pp. 104-110.

J. Karimov and M. Ozbayoglu, “Clustering Quality Improvement of k-means Using a Hybrid Evolutionary Model,” Procedia Comput. Sci., vol. 61, pp. 38–45, 2015.

T. Niknam and B. Amiri, “An efficient hybrid approach based on PSO, ACO and k-means for cluster analysis,” Appl. Soft Comput. J., vol. 10, no. 1, pp. 183–197, 2010.

R. J. Kuo, M. J. Wang, and T. W. Huang, “An application of particle swarm optimization algorithm to clustering analysis,” Soft Comput., vol. 15, no. 3, pp. 533–542, 2011.

G. Armano and M. R. Farmani, “Multiobjective clustering analysis using particle swarm optimization,” Expert Syst. Appl., vol. 55, pp. 184–193, 2016.

H. Li, H. He, and Y. Wen, “Dynamic Particle Swarm Optimization and K-means Clustering Algorithm for Image Segmentation,” Opt. - Int. J. Light Electron Opt., vol. 126, no. 24, pp. 4817–4822, 2015.

M. Y. Cheng, K. Y. Huang, and H. M. Chen, “K-means particle swarm optimization with embedded chaotic search for solving multidimensional problems,” Appl. Math. Comput., vol. 219, no. 6, pp. 3091–3099, 2012.

C.-Y. Chiu, Y.-F. Chen, I.-T. Kuo, and H. C. Ku, “An Intelligent Market Segmentation System Using K-Means and Particle Swarm Optimization,” Expert Syst. Appl., vol. 36, no. 3, pp. 4558–4565, 2009.

J. Han and K. Micheline, Data Mining: Concepts and Techniques, Second Edi., no. Second Edition. Morgan Kaufmann Publishers, 2006.

A. Karami and M. Guerrero-Zapata, “A fuzzy anomaly detection system based on hybrid PSO-Kmeans algorithm in content-centric networks,” Neurocomputing, vol. 149, no. PC, pp. 1253–1269, 2015.

L. Hidayat dan W. F. Mahmudy, “Grouping personality test result data bus driver using a genetic algorithm,” Jurnal Teknologi Informasi dan Ilmu Komputer, vol. 3, no. 3, pp.163-168.

M. Anggara, H. Sujiani, and H. Nasution, “Selection of measure distance in k-means clustering for grouping members in alvaro fitness”, Jurnal Sistem dan Teknologi Informasi (JUSTIN)vol. 1, no. 1, pp. 1–6, 2016

Wahyuni, I., Auliya Y. A., Rahmi, A. dan Mahmudy, W. F. “Clustering based on the level of liquidity of bank customers using pso hybrid k-means”, Jurnal Ilmiah Teknologi dan Informasi Asia (JITIKA), vol. 10, no. 1, pp. 20–30, 2016

W. F.Mahmudy, R. M. Marian and L. H. S. Loung, "Hybrid genetic algorithms for part type selection and machine loading problems with alternative production plans in flexible manufacturing system", ECTI Transactions on Computer and Information Technology (ECTI-CIT), vol. 8, no. 1, pp. 80-93




DOI: http://dx.doi.org/10.25126/jitecs.20194183