Hard and soft updating centroids for clustering Y-short tandem repeats (Y-STR) data

Author

Seman, Ali ; Bakar, Zainab Abu ; Daud, Noorizam

Author_Institution

Centre for Comput. Sci. Studies, Univ. Teknol. MARA (UiTM), Shah Alam, Malaysia

fYear

2010

fDate

5-7 Dec. 2010

Firstpage

6

Lastpage

11

Abstract

This paper compares hard and soft updating centroids for clustering Y-STR data. The hard centroids represented by New Fuzzy k-Modes clustering algorithm, whereas the soft centroids represented through k-Population algorithm. These two algorithms are experimented through two datasets, Y-STR haplogroups and Y-STR Surnames. The results show that the soft centroid performance is better than the hard centroid for Y-STR data. The soft centroid produces 86.3% of the average clustering accuracy as compared 84.3% of the new fuzzy k-Modes algorithm. However, the overall result shows that the hard updating clustering is better than the soft updating clustering while clustering Y-STR data.

Keywords

fuzzy logic; fuzzy reasoning; pattern clustering; A-population algorithm; Y-STR haplogroups; Y-STR surnames; clustering Y-short tandem repeat data; clustering accuracy; fuzzy A-modes clustering algorithm; hard updating centroid clustering; soft updating centroid clustering; Accuracy; Algorithm design and analysis; Clustering algorithms; DNA; Equations; Indexes; Mathematical model; Clustering algorithm; Y-STR; categorical data; hard and soft centroids;

fLanguage

English

Publisher

ieee

Conference_Titel

Open Systems (ICOS), 2010 IEEE Conference on

Conference_Location

Kuala Lumpur

Print_ISBN

978-1-4244-9193-3

Type

conf

DOI

10.1109/ICOS.2010.5720055

Filename

5720055