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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Faculty of Computer and Mathematical Sciences,
2010.
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|---|---|---|---|
| 001 | repouitm_11101 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Seman, Ali |e author |
| 700 | 1 | 0 | |a Abu Bakar, Zainab |e author |
| 700 | 1 | 0 | |a Mohd. Sapawi, Azizian |e author |
| 245 | 0 | 0 | |a Centre-based hard clustering algorithms for Y-STR data / Ali Seman, Zainab Abu Bakar and Azizian Mohd. Sapawi |
| 260 | |b Faculty of Computer and Mathematical Sciences, |c 2010. | ||
| 500 | |a https://ir.uitm.edu.my/id/eprint/11101/1/11101.pdf | ||
| 520 | |a 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. | ||
| 546 | |a en | ||
| 655 | 7 | |a Article |2 local | |
| 655 | 7 | |a PeerReviewed |2 local | |
| 787 | 0 | |n https://ir.uitm.edu.my/id/eprint/11101/ | |
| 787 | 0 | |n https://mjoc.uitm.edu.my/ | |
| 856 | 4 | 1 | |u https://ir.uitm.edu.my/id/eprint/11101/ |z Link Metadata |