Centre-Based Hard and Soft Clustering Approaches for Y-STR Data

This paper presents Centre-based clustering approaches for clustering Y-STR data. The main goal is to investigate and observe the performance of the fundamental clustering approaches when partitioning Y-STR data. Two fundamental Centre-based hard clustering approaches, k-Means and k-Modes algorithms, and two fundamental Centre-based soft clustering approaches, fuzzy k-Means and fuzzy k-Modes algorithms were chosen for evaluation of Y-STR haplogroup and Y-STR Surname datasets. The results show that the soft k-Means clustering algorithm produces the best average of the clustering accuracy (99.62%) for Y-STR haplogroup data as well Y-STR surname data (97.61%). The overall results show that the soft clustering approach is better (92.11%) than the hard clustering approach (81.20%) in clustering Y-STR data. However, the approach for clustering Y-STR data should be further investigated to find the best way of achieving 100% of the clustering results.