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Open Access Research article

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

Ali Seman1*, Zainab Abu Bakar1 and Mohamed Nizam Isa2

Author Affiliations

1 Center for Computer Sciences, Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA (UiTM), 40450 Shah Alam, Selangor, Malaysia

2 Medical Faculty, Masterskill University College of Health Sciences, No. 6, Jalan Lembah, Bandar Seri Alam, 81750, Johor Bahru, Johor, Malaysia

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BMC Research Notes 2012, 5:557  doi:10.1186/1756-0500-5-557

Published: 6 October 2012

Abstract

Background

Y-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.

Results

Our 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).

Conclusions

The 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.

Keywords:
Algorithms; Bioinformatics; Clustering; Optimization; Data mining