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Trends in Bioinformatics
  Year: 2012 | Volume: 5 | Issue: 2 | Page No.: 47-52
DOI: 10.3923/tb.2012.47.52
 
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Evaluation of k-modes-type Algorithms for Clustering Y-Short Tandem Repeats Data
Ali Seman, Zainab Abu Bakar and Mohamed Nizam Isa

Abstract:
This paper reports on the experimental results of the k-modes-type algorithms for partitioning Y-Short Tandem Repeats (Y-STR) data. The results were based on the clustering accuracy scores of five hard and three soft k-modes-type algorithms. Six Y-Short Tandem Repeats data sets were used as a benchmark for the evaluation. The results clearly indicated that the soft k-modes-type clustering algorithms are the most reliable algorithms for partitioning Y-STR data.
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How to cite this article:

Ali Seman, Zainab Abu Bakar and Mohamed Nizam Isa, 2012. Evaluation of k-modes-type Algorithms for Clustering Y-Short Tandem Repeats Data. Trends in Bioinformatics, 5: 47-52.

DOI: 10.3923/tb.2012.47.52

URL: https://scialert.net/abstract/?doi=tb.2012.47.52

 
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