title: An efficient clustering algorithm for partitioning Y-short tandem repeats data.
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.
authors.affiliation: Center for Computer Sciences, Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia. aliseman@tmsk.uitm.edu.my
authors.forename: Ali
authors.initials: A
authors.lastname: Seman
authors.forename: Zainab Abu
authors.initials: ZA
authors.lastname: Bakar
authors.forename: Mohamed Nizam
authors.initials: MN
authors.lastname: Isa
mesh_terms: D000465:Algorithms; D000483:Alleles; D041322:Chromosomes, Human, Y; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D006239:Haplotypes; D006801:Humans; D018895:Microsatellite Repeats; D010363:Pattern Recognition, Automated / Q000706:statistics & numerical data*