Clustering for binary data sets by using genetic algorithm-incremental K-means

Saharan, S. and Baragona, R. and Nor, M. E. and Salleh, R. M. and Asrah, N. M. (2018) Clustering for binary data sets by using genetic algorithm-incremental K-means. Journal of Physics: Conference Series, 995. pp. 1-5. ISSN 1742-6588

Text AJ 2018 (308).pdf Restricted to Registered users only Download (443kB) | Request a copy

Abstract

This research was initially driven by the lack of clustering algorithms that specifically focus in binary data. To overcome this gap in knowledge, a promising technique for analysing this type of data became the main subject in this research, namely Genetic Algorithms (GA). For the purpose of this research, GA was combined with the Incremental Kmeans (IKM) algorithm to cluster the binary data streams. In GAIKM, the objective function was based on a few sufficient statistics that may be easily and quickly calculated on binary numbers. The implementation of IKM will give an advantage in terms of fast convergence. The results show that GAIKM is an efficient and effective new clustering algorithm compared to the clustering algorithms and to the IKM itself. In conclusion, the GAIKM outperformed other clustering algorithms such as GCUK, IKM, Scalable K-means (SKM) and K-means clustering and paves the way for future research involving missing data and outliers.

Item Type:	Article
Uncontrolled Keywords:	NIL
Subjects:	Q Science > QA Mathematics > QA273-280 Probabilities. Mathematical statistics
Divisions:	Faculty of Applied Science and Technology > Department of Mathematics and Statistics
Depositing User:	UiTM Student Praktikal
Date Deposited:	20 Jan 2022 06:36
Last Modified:	20 Jan 2022 06:36
URI:	http://eprints.uthm.edu.my/id/eprint/5691

Actions (login required)

View Item