Centre-based hard clustering algorithms for Y-STR data / Ali Seman, Zainab Abu Bakar and Azizian Mohd. Sapawi
This paper presents Centre-based hard clustering approaches for clustering Y-STR data. Two classical partitioning techniques: Centroid-based partitioning technique and Representative object-based partitioning technique are evaluated. The k-Means and the k-Modes algorithms are the fundamental algorit...
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| Main Authors: | , , |
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| 格式: | Article |
| 语言: | English |
| 出版: |
Faculty of Computer and Mathematical Sciences
2010
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| 在线阅读: | https://ir.uitm.edu.my/id/eprint/11101/1/11101.pdf https://ir.uitm.edu.my/id/eprint/11101/ https://mjoc.uitm.edu.my/ |
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| 总结: | This paper presents Centre-based hard clustering approaches for clustering Y-STR data. Two classical partitioning techniques: Centroid-based partitioning technique and Representative object-based partitioning technique are evaluated. The k-Means and the k-Modes algorithms are the fundamental algorithms for the centroid-based partitioning technique, whereas the k-Medoids is a representative object-based partitioning technique. The three algorithms above are experimented and evaluated in partitioning Y-STR haplogroups and Y-STR Surname data. The overall results show that the centroid-based partitioning technique is better than the representative object-based partitioning technique in clustering Y-STR data. |
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