Optimizing K-Means initial number of cluster based  heuristic approach: literature review analysis perspective

Muhammad Modi Lakulu

QR Code Link :
Type :	article
Subject :	N Fine Arts
Main Author :	Muhammad Modi Lakulu
Additional Authors :	Ramlah Mailok Harunur Rosyid
Title :	Optimizing K-Means initial number of cluster based heuristic approach: literature review analysis perspective

Place of Production :	Tanjong Malim
Publisher :	Fakulti Seni, Komputeran dan Industri Kreatif
Year of Publication :	2019
Corporate Name :	Universiti Pendidikan Sultan Idris

Abstract : Universiti Pendidikan Sultan Idris

One popular clustering technique -the K-means widely use in educational scope to clustering and mapping document, data, and user performance in skill. K-means clustering is one of the classical and most widely used clustering algorithms shows its efficiency in many traditional applications its defect appears obviously when the data set to become much more complicated. Based on some research on K-means algorithm shows that Number of a cluster of K-means cannot easily be specified in much real-world application, several algorithms requiring the number of cluster as a parameter cannot be effectively employed. The aim of this paper describes the perspective K-means problems underlying research. Literature analysis of previous studies suggesting that selection of the number of clusters randomly cause problems such as suitable producing globular cluster, less efficient if as the number of cluster grow K-means clustering becomes untenable. From those literature reviews, the heuristic optimization will be approached to solve an initial number of cluster randomly.

References

[1] A. Dutt, S. Aghabozrgi, M. Akmal, B. Ismail, and H. Mahroeian, “Clustering Algorithms Applied,” in Educational Data Mining, vol. 5, no. 2, pp. 112–116, 2015. [Online]. Available: https://doi.org/10.7763/ JIEE.2015.V5.513.

[2] D. Roy, “Synthesis of clustering techniques in educational data mining,” 2017.

[3] S. ÿaL·atay, and F. Y. Gürocak, “Is CEFR Really over There?,” Procedia - Social and Behavioral Sciences, vol. 232, pp. 705–712, April 2016. [Online]. Available: https://doi.org/10.1016/j.sbspro.2016.10.096.

[4] Dunham, M. H, “Data Mining Introductory and Advanced Topics,” Prentice Hall/Pearson Education, 2003.

[5] J.N.D. Macqueen, “Some Methods For Classification And Analysis Of Multivariate Observations", vol. 233, no. 233, pp. 281–297.

[6] D. Sharmilarani and N. Kousika, “Modified K-Means Algorithm for Automatic Stimation of Number of Clusters Using Advanced Visual Assessment of Cluster Tendency”, pp. 236–239, 2014.

[7] X. Wang, Y. Jiao, and S. Fei, “Estimation of Clusters Number and Initial Centers of K-means Algorithm Using Watershed Method, no. 0, 2015. [Online]. Available: https://doi.org/10.1109/ DCABES.2015.132.

[8] R. Forsati, A. Keikha, and M. Shmasfard, “Accepted Manuscript An Improved Bee Colony Optimization Algorithm with an Application to Document Clustering,” Neurocomputing, 2015. [Online]. Available: https://doi.org/10.1016/j.neucom.2015.02.048.

[9] J. Xiao, Y. Yan, J. Zhang, and Y. Tang, “Expert Systems with Applications A quantum-inspired genetic algorithm for k -means clustering,” Expert Systems With Applications, vol. 37, no. 7, pp. 4966–4973, 2010. [Online]. Available: https://doi.org/10.1016/j.eswa.2009.12.017.

[10] K. R. Zetty, “An efficient k-means clustering algorithm,” vol. 29, pp. 1385–1391, 2008. [Online]. Available: https:// doi.org/10. 1016/j.patrec.2008.02.014.

[11] D. Karaboga, and C. Ozturk, “A novel clustering approach,” Artificial Bee Colony (ABC ) algorithm, vol. 11, pp, 652-657, 2011. [Online]. Available: https://doi.org/10.1016/ j.asoc.2009.12.025.

[12] A. Kishor, P. K. Singh, and J. Prakash, “NSABC: Non-dominated sorting based multi-objective artificial bee colony algorithm and its application in data clustering,” Neurocomputing, vol. 216, pp. 514-533, 2016. [Online]. Available: https://doi.org/10.1016/j.neucom.2016.08.003.

[13] S. M. Laszlo, “A genetic algorithm using hyper-quadtrees for low-dimensional k-means clustering,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 4, pp. 533–543, 2006.

[14] Y. Liu, X. Wu, and Y. Shen, “Automatic clustering using genetic algorithms,” Applied Mathematics and Computation, vol. 218, no. 4, pp. 1267–1279, 2011. [Online]. Available: https://doi.org/10.1016/j.amc.2011.06.007.

This material may be protected under Copyright Act which governs the making of photocopies or reproductions of copyrighted materials.
You may use the digitized material for private study, scholarship, or research.

Back to previous page