Centre-based hard clustering algorithms for Y-STR data / Ali Seman, Zainab Abu Bakar and Azizian Mohd. Sapawi

• Main Authors: Seman, Ali, Abu Bakar, Zainab, Mohd. Sapawi, Azizian
• Format: Article
• Language: English
• Published: Faculty of Computer and Mathematical Sciences 2010
• Online Access: https://ir.uitm.edu.my/id/eprint/11101/1/11101.pdf; https://ir.uitm.edu.my/id/eprint/11101/; https://mjoc.uitm.edu.my/
• Institution: Universiti Teknologi Mara

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.