Clustering for binary data sets by using genetic algorithm-incremental K-means
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 r...
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| Format: | Article |
| Language: | en |
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IOP Publishing
2018
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| Online Access: | http://eprints.uthm.edu.my/5691/1/AJ%202018%20%28308%29.pdf http://eprints.uthm.edu.my/5691/ |
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| author | Saharan, S. Baragona, R. Nor, M. E. Salleh, R. M. Asrah, N. M. |
| author_facet | Saharan, S. Baragona, R. Nor, M. E. Salleh, R. M. Asrah, N. M. |
| author_sort | Saharan, S. |
| building | UTHM Library |
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| content_provider | Universiti Tun Hussein Onn Malaysia |
| content_source | UTHM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | 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. |
| format | Article |
| id | my.uthm.eprints-5691 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | en |
| publishDate | 2018 |
| publisher | IOP Publishing |
| record_format | eprints |
| spelling | my.uthm.eprints-56912022-01-20T06:36:56Z http://eprints.uthm.edu.my/5691/ Clustering for binary data sets by using genetic algorithm-incremental K-means Saharan, S. Baragona, R. Nor, M. E. Salleh, R. M. Asrah, N. M. QA273-280 Probabilities. Mathematical statistics 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. IOP Publishing 2018 Article PeerReviewed text en http://eprints.uthm.edu.my/5691/1/AJ%202018%20%28308%29.pdf 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 |
| spellingShingle | QA273-280 Probabilities. Mathematical statistics Saharan, S. Baragona, R. Nor, M. E. Salleh, R. M. Asrah, N. M. Clustering for binary data sets by using genetic algorithm-incremental K-means |
| title | Clustering for binary data sets by using genetic algorithm-incremental K-means |
| title_full | Clustering for binary data sets by using genetic algorithm-incremental K-means |
| title_fullStr | Clustering for binary data sets by using genetic algorithm-incremental K-means |
| title_full_unstemmed | Clustering for binary data sets by using genetic algorithm-incremental K-means |
| title_short | Clustering for binary data sets by using genetic algorithm-incremental K-means |
| title_sort | clustering for binary data sets by using genetic algorithm-incremental k-means |
| topic | QA273-280 Probabilities. Mathematical statistics |
| url | http://eprints.uthm.edu.my/5691/1/AJ%202018%20%28308%29.pdf http://eprints.uthm.edu.my/5691/ |
| url_provider | http://eprints.uthm.edu.my/ |