Performance Evaluations of κ-Approximate Modal Haplotype Type Algorithms for Clustering Categorical Data
Ali Seman, Azizian Mohd Sapawi, Mohd Zaki Bin Salleh · Research Journal of Information Technology · 2015
The effectiveness of the performance of κ-Approximate Modal Haplotype (κ-AMH)-type algorithms for clustering Y-short tandem repeats (Y-STR) of categorical data has been demonstrated previously.However, newly introduced κ-AMH-type algorithms, including the new κ-AMH I (Nκ-AMH 1), the new κ-AMH II (Nκ-AMH II) and the new κ-AMH III (Nκ-AMH III), are derived from the same κ-AMH optimization and fuzzy procedures but with the inclusion of two new methods, namely, new initial center selection and new dominant weighting methods.This study evaluates and presents the performance of κ-AMH-type algorithms for clustering five categorical data sets-namely, soybean, zoo, hepatitis, voting and breast.The performance criteria include accuracy, precision and recall analyses.Overall, κ-AMH-type algorithms perform well when clustering all of the categorical data sets mentioned above.Specifically, the N κ-AMH I algorithm exhibits the best performance when clustering the five categorical data sets; this algorithm obtained the highest combined mean accuracy score (at 0.9130), compared to those of κ-AMH (0.8971), N κ-AMH II (0.8885) and N κ-AMH III (0.9011).This high score is associated with the newly introduced initial center selection, combined with the original dominant weighting method.These results present a new and significant benchmark, indicating that κ-AMH-type algorithms can be generalized for any categorical data.