Centre-based clustering for Y-Short Tandem Repeats (Y-STR) as numerical and categorical data
Ali Seman, Zainab Abu Bakar, Azizian Mohd Sapawi · 2010
Centre-based clustering is among the most applicable method for partitioning objects into homogenous groups. This paper presents two Centre-based clustering; K-Means and K-Modes algorithms to investigate and evaluate the clustering results of Y-STR data. The main goal of this paper is to compare the accuracy of clustering Y-STR results for different types of data: numerical and categorical data. The results show that the Y-STR data is more favour to categorical data. The accuracy of the Y-STR, treated as categorical data is 49%, whereas the numerical data is only a 26% chance producing a good clustering result. However, the amount of time taken by numerical data is much better compared to categorical data.