An efficient clustering algorithm for partitioning Y-short tandem repeats data

Abstract

BackgroundY-Short Tandem Repeats (Y-STR) data consist of many similar and almost similar objects. This characteristic of Y-STR data causes two problems with partitioning: non-unique centroids and local minima problems. As a result, the existing partitioning algorithms produce poor clustering results.ResultsOur new algorithm, called k-Approximate Modal Haplotypes (k-AMH), obtains the highest clustering accuracy scores for five out of six datasets, and produces an equal performance for the remaining dataset. Furthermore, clustering accuracy scores of 100% are achieved for two of the datasets. The k-AMH algorithm records the highest mean accuracy score of 0.93 overall, compared to that of other algorithms: k-Population (0.91), k-Modes-RVF (0.81), New Fuzzy k-Modes (0.80), k-Modes (0.76), k-Modes-Hybrid 1 (0.76), k-Modes-Hybrid 2 (0.75), Fuzzy k-Modes (0.74), and k-Modes-UAVM (0.70).ConclusionsThe partitioning performance of the k-AMH algorithm for Y-STR data is superior to that of other algorithms, owing to its ability to solve the non-unique centroids and local minima problems. Our algorithm is also efficient in terms of time complexity, which is recorded as O(km(n-k)) and considered to be linear.

Towards Development of Clustering Applications for Large-Scale Comparative Genotyping and Kinship Analysis Using Y-Short Tandem Repeats

“…However, where the sample size is large and/or there are multiple samples, multi-criteria analyses such as supervised and unsupervised learning methods produce results that are more informative. Indeed, several methods for grouping multiple samples of Y-STR data automatically have been reported (Schlecht et al, 2008 ; Seman et al, 2010a ; 2012 ; 2013a ). In the supervised learning method, Y-STR data can be classified by haplogroup via the decision tree method (Schlecht et al, 2008 ; Seman et al, 2013a ), Bayesian modeling, and support vector machines (Schlecht et al, 2008 ).…”

“…In the supervised learning method, Y-STR data can be classified by haplogroup via the decision tree method (Schlecht et al, 2008 ; Seman et al, 2013a ), Bayesian modeling, and support vector machines (Schlecht et al, 2008 ). Similarly, unsupervised learning methods can be used to cluster Y-STR data by similar genetic distances (Seman et al, 2010a ; 2010b ; 2010c ; 2010d ; 2012 ).…”

First Y-Short Tandem Repeat Categorical Dataset for Clustering Applications

“…The outcomes of this result indicate that the Y-STR data are quite unique compared to other categorical data, characterizing many similar and almost similar objects. This uniqueness of the Y-STR data has caused the existing clustering algorithms to produce poor clustering results (see the detailed problems of clustering Y-STR data in [15]). As a result, we have recently proposed a new algorithm called -Approximate Modal Haplotype ( -AMH) for clustering six Y-STR data [15].…”

“…This uniqueness of the Y-STR data has caused the existing clustering algorithms to produce poor clustering results (see the detailed problems of clustering Y-STR data in [15]). As a result, we have recently proposed a new algorithm called -Approximate Modal Haplotype ( -AMH) for clustering six Y-STR data [15]. Letting these Y-STR dataset items be a benchmark, the -AMH algorithm has been proven as an efficient clustering algorithm for partitioning Y-STR data.…”

“…Letting these Y-STR dataset items be a benchmark, the -AMH algorithm has been proven as an efficient clustering algorithm for partitioning Y-STR data. Tables 1 and 2 show the clustering results, comparing the -AMH algorithm and the other eight clustering algorithms as reported in [15]. Thus, the objective of this paper is to give the detailed insight of the six Y-STR dataset items used in the previous benchmarking results of clustering applications.…”