Main Article Content
Clustering is a technique used to classify data into clusters based on their similarities. K-means is a clustering algorithm method that classifies the objects based on their closest distance to the cluster center to the groups that have most similarities among the members. In addition, K-means is also the most widely used clustering algorithm due to its ease of implementation. However, the process of selecting the centroid on K-means still randomly. This results K-means is often trapped in local minimum conditions. Genetic algorithm is used in this research as a metaheuristic method where the algorithm can support K-means in reaching global optimum function. Besides, the stratified sampling is also used in this research, where the sampling functions by dividing the population into homogeneous areas using stratification variables. The validation value of the proposed method with iris dataset is 0.417, while the K-means is only 0.662.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work
 A. K. Jain, "Data clustering: 50 years beyond K-means," Pattern Recognit. Lett., vol. 31, no. 8, pp. 651–666, Jun. 2010.
 J. Wu, Advances in K-means Clustering. A Data Mining Thinking. 2012.
 T. Le and T. Altman, "A new initialization method for the Fuzzy C-Means Algorithm using Fuzzy Subtractive Clustering," in Proc. International Conference on Information and Knowledge Engineering, 2011, vol. Vol. 1, pp. 144–150.
 W. Huai-bin, Y. Hong-liang, X. Zhi-jian, and Y. Zheng, "A clustering algorithm use SOM and k-means in intrusion detection," 2010 Int. Conf. E-bus. E-Government, no. 2007, pp. 1281–1284, May 2010.
 M. E. Celebi, H. A. Kingravi, and P. A. Vela, "A comparative study of efficient initialization methods for the k-means clustering algorithm," Expert Syst. Appl., vol. 40, no. 1, pp. 200–210, 2013.
 X. Wu et al., "Top 10 algorithms in data mining," Knowl. Inf. Syst., vol. 14, no. 1, pp. 1–37, 2008.
 M. E. Celebi and H. A. Kingravi, "Order-Invariant Initialization Methods for the K-Means Clustering Algorithm."
 J. Han and M. Kamber, "Data mining: concepts and techniques," vol. 49, no. 6, pp. 49-3305-49–3305, Feb. 2012.
 P. Camus, F. J. Mendez, R. Medina, and A. S. Cofiño, "Analysis of clustering and selection algorithms for the study of multivariate wave climate," Coast. Eng., vol. 58, no. 6, pp. 453–462, 2011.
 J. H. Holland, "Genetic algorithm," Sci. Am., no. July, 1992.
 M. Melanie, "An introduction to genetic algorithms," Cambridge, Massachusetts London, England, Fifth Print. 3, 1999.
 M. A. Rahman and M. Z. Islam, "A hybrid clustering technique combining a novel genetic algorithm with K-Means," Knowledge-Based Syst., vol. 71, pp. 345–365, Nov. 2014.
 M. Breaban and H. Luchian, "A unifying criterion for unsupervised clustering and feature selection," Pattern Recognit., vol. 44, no. 4, pp. 854–865, 2011.
 P. Yang, R. Science, and M. N. M. R. Divisiont, "Partition Testing , Cluster Stratified Analysis * Sampling , and," 1993.
 D. L. Davies and D. W. Bouldin, "A cluster separation measure.," IEEE Trans. Pattern Anal. Mach. Intell., vol. 1, no. 2, pp. 224–227, 1979.