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Hybrid Particle Swarm Optimization and k-Means Clustering for Education Quality Mapping

by Muhammad Lintang Cahyo, Jatmiko Endro Suseno, Oky Dwi Nurhayati
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 180 - Number 42
Year of Publication: 2018
Authors: Muhammad Lintang Cahyo, Jatmiko Endro Suseno, Oky Dwi Nurhayati
10.5120/ijca2018917123

Muhammad Lintang Cahyo, Jatmiko Endro Suseno, Oky Dwi Nurhayati . Hybrid Particle Swarm Optimization and k-Means Clustering for Education Quality Mapping. International Journal of Computer Applications. 180, 42 ( May 2018), 39-45. DOI=10.5120/ijca2018917123

@article{ 10.5120/ijca2018917123,
author = { Muhammad Lintang Cahyo, Jatmiko Endro Suseno, Oky Dwi Nurhayati },
title = { Hybrid Particle Swarm Optimization and k-Means Clustering for Education Quality Mapping },
journal = { International Journal of Computer Applications },
issue_date = { May 2018 },
volume = { 180 },
number = { 42 },
month = { May },
year = { 2018 },
issn = { 0975-8887 },
pages = { 39-45 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume180/number42/29415-2018917123/ },
doi = { 10.5120/ijca2018917123 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:03:26.530758+05:30
%A Muhammad Lintang Cahyo
%A Jatmiko Endro Suseno
%A Oky Dwi Nurhayati
%T Hybrid Particle Swarm Optimization and k-Means Clustering for Education Quality Mapping
%J International Journal of Computer Applications
%@ 0975-8887
%V 180
%N 42
%P 39-45
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Quality mapping on education can be useful information to evaluate how well the quality of education is attained in each school. Solutions to overcome this problem required an information system for mapping the quality of education based on the five categories of quality achievement levels set by the government of Indonesia. This study was conducted using 200 school datasets in the city of Semarang. the study used the test parameters of input 20 particles, 40 particles, 80 particles and 160 particles. parameters were tested using PSO and PSO + k-Means methods. As a result, the use of the 20 particles parameter provides an optimal solution in grouping data at a central point.

References
  1. H. Jiawei, K. Micheline, and P. Jian, DATA MINING (Concept and Techniques), vol. 3, no. 13. 2012.
  2. C. Cobos, H. Muñoz-Collazos, R. Urbano-Muñoz, M. Mendoza, E. León, and E. Herrera-Viedma, “Clustering of web search results based on the cuckoo search algorithm and Balanced Bayesian Information Criterion,” Inf. Sci. (Ny)., vol. 281, pp. 248–264, 2014.
  3. L. Rokach and O. Maimon, The Data Mining and Knowledge Discovery Handbook. 2010.
  4. B. Mirkin, Clustering for Data Mining - A Data Recovery Approach. 2005.
  5. O. R. Vincent, A. S. Makinde, O. S. Salako, and O. D. Oluwafemi, “A self-adaptive k-Means classifier for business incentive in a fashion design environment,” Appl. Comput. Informatics, 2017.
  6. M. Capó, A. Pérez, and J. A. Lozano, “An efficient approximation to the K-Means clustering for massive data,” Knowledge-Based Syst., vol. 117, pp. 56–69, 2017.
  7. B. Everitt, “Cluster analysis,” Qual. Quant., vol. 14, no. 1, pp. 75–100, 1980.
  8. N. Kamel, I. Ouchen, and K. Baali, “A sampling-PSO-K-Means algorithm for document clustering,” in Advances in Intelligent Systems and Computing, 2014, vol. 238, pp. 45–54.
  9. B. F. . Solaiman and A. . Sheta, “Energy optimization in wireless sensor networks using a hybrid K-Means PSO clustering algorithm,” Turkish J. Electr. Eng. Comput. Sci., vol. 24, no. 4, pp. 2679–2695, 2016.
  10. S. Kalyani and K. S. Swarup, “Particle swarm optimization based K-Means clustering approach for security assessment in power systems,” Expert Syst. Appl., vol. 38, no. 9, pp. 10839–10846, 2011.
  11. X. Cui and T. E. Potok, “Document Clustering Analysis Based on Hybrid PSO+K-Means Algorithm,” Engineering, pp. 185–191, 2005.D. W. Van Der Merwe and A. P. Engelbrecht, “Data clustering using particle swarm optimization,” 2003 Congr. Evol. Comput. 2003 CEC 03, vol. 1, pp. 215–220, 2003.
  12. Ahmadyfard and H. Modares, “Combining PSO and k-Means to enhance data clustering,” in 2008 International Symposium on Telecommunications, IST 2008, 2008, pp. 688–691.
  13. P. Arora, Deepali, and S. Varshney, “Analysis of K-Means and K-Medoids Algorithm for Big Data,” in Physics Procedia, 2016, vol. 78, pp. 507–512.
  14. B. S. Serapião, G. S. Corrêa, F. B. Gonçalves, and V. O. Carvalho, “Combining K-Means and K-Harmonic with Fish School Search Algorithm for data clustering task on graphics processing units,” Appl. Soft Comput. J., vol. 41, pp. 290–304, 2016.
  15. S. J. Redmond and C. Heneghan, “A method for initialising the K-Means clustering algorithm using kd-trees,” Pattern Recognit. Lett., vol. 28, no. 8, pp. 965–973, 2007.
  16. J. Kennedy and R. Eberhart, “Particle swarm optimization,” Neural Networks, 1995. Proceedings., IEEE Int. Conf., vol. 4, pp. 1942–1948 vol.4, 1995.
  17. R. C. Eberhart and Yuhui Shi, “Particle swarm optimization: developments, applications and resources,” Proc. 2001 Congr. Evol. Comput. (IEEE Cat. No.01TH8546), vol. 1, pp. 81–86, 2001.
Index Terms

Computer Science
Information Sciences

Keywords

Information system clustering particle swarm optimization k-Means clustering

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