Attribute value weighting in K-modes clustering for Y-short tandem repeats (Y-STR) surname

Seman, Ali; Bakar, Zainab Abu; Sapawi, Azizian Mohd

doi:10.1109/itsim.2010.5561471

Cited by 4 publications

(5 citation statements)

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“…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 ).…”

Section: Introductionmentioning

confidence: 99%

“…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 ).…”

Section: Introductionmentioning

confidence: 99%

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

Seman

Sapawi

Salleh

2015

OMICS: A Journal of Integrative Biology

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Y-chromosome short tandem repeats (Y-STRs) are genetic markers with practical applications in human identification. However, where mass identification is required (e.g., in the aftermath of disasters with significant fatalities), the efficiency of the process could be improved with new statistical approaches. Clustering applications are relatively new tools for large-scale comparative genotyping, and the k-Approximate Modal Haplotype (k-AMH), an efficient algorithm for clustering large-scale Y-STR data, represents a promising method for developing these tools. In this study we improved the k-AMH and produced three new algorithms: the Nk-AMH I (including a new initial cluster center selection), the Nk-AMH II (including a new dominant weighting value), and the Nk-AMH III (combining I and II). The Nk-AMH III was the superior algorithm, with mean clustering accuracy that increased in four out of six datasets and remained at 100% in the other two. Additionally, the Nk-AMH III achieved a 2% higher overall mean clustering accuracy score than the k-AMH, as well as optimal accuracy for all datasets (0.84–1.00). With inclusion of the two new methods, the Nk-AMH III produced an optimal solution for clustering Y-STR data; thus, the algorithm has potential for further development towards fully automatic clustering of any large-scale genotypic data.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Seman

Sapawi

Salleh

2015

OMICS: A Journal of Integrative Biology

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“…The Y-STR data have been applied and used in clustering Y-surname and Y-haplogroup applications. Initial benchmarking results of clustering Y-STR data have been reported (see, e.g., [8][9][10][11][12]). Furthermore, the Y-STR data and their clustering results have also been published in a journal called Journal of Genetic Genealogy, a journal of genetic genealogical community [13].…”

Section: Introductionmentioning

confidence: 99%

First Y-Short Tandem Repeat Categorical Dataset for Clustering Applications

Seman

Bakar

Isa³

2013

Dataset Papers in Biology

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The Y-chromosome short tandem repeat (Y-STR) data are mainly collected for a performance benchmarking result in clustering methods. There are six Y-STR dataset items, divided into two categories: Y-STR surname and Y-haplogroup data presented here. The Y-STR data are categorical, unique, and different from the other categorical data. They are composed of a lot of similar and almost similar objects. This characteristic of the Y-STR data has caused certain problems of the existing clustering algorithms in clustering them.

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“…For example, Schlecht et al [12] used machine learning techniques to classify Y-STR fragments into related groups. Furthermore, Seman et al [13][14][15][16][17][18][19] used partitional clustering techniques to group Y-STR data by the number of repeats, a method used in genetic genealogy applications. In this study, we continue efforts to partition the Y-STR data based on the partitional clustering approaches carried out in [13][14][15][16][17][18][19].…”

mentioning

confidence: 99%

“…Furthermore, Seman et al [13][14][15][16][17][18][19] used partitional clustering techniques to group Y-STR data by the number of repeats, a method used in genetic genealogy applications. In this study, we continue efforts to partition the Y-STR data based on the partitional clustering approaches carried out in [13][14][15][16][17][18][19]. Recently, we have also evaluated eight partitional clustering algorithms over six Y-STR datasets [19].…”

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confidence: 99%

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

Seman¹,

Bakar²,

Isa³

2012

BMC Research Notes

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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.

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Attribute value weighting in K-modes clustering for Y-short tandem repeats (Y-STR) surname

Cited by 4 publications

References 11 publications

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

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

First Y-Short Tandem Repeat Categorical Dataset for Clustering Applications

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

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