DocumentCode :
3723949
Title :
Efficient agglomerative hierarchical clustering for biological sequence analysis
Author :
Thuy-Diem Nguyen;Chee-Keong Kwoh
Author_Institution :
School of Computer Engineering, Nanyang Technological University, Singapore
fYear :
2015
Firstpage :
1
Lastpage :
3
Abstract :
Cluster analysis is an important data mining technique widely used for pattern recognition and information retrieval. In the literature, over a hundred clustering algorithms have been developed to target input datasets with different characteristics. Among these algorithms, the hierarchical clustering method is particularly useful for analyzing genetic datasets in evolutionary biology studies because of the inherent hierarchical relationships amongst the genetic sequences extracted from related organisms. However, this algorithm is computational expensive in terms of both execution time and particularly memory usage. This paper summarizes our experience in using parallel computing technologies with new algorithms to perform hierarchical sequence clustering in a more effective way without compromising the accuracy of the results.
Keywords :
Genetics
Publisher :
ieee
Conference_Titel :
TENCON 2015 - 2015 IEEE Region 10 Conference
ISSN :
2159-3442
Print_ISBN :
978-1-4799-8639-2
Electronic_ISBN :
2159-3450
Type :
conf
DOI :
10.1109/TENCON.2015.7373194
Filename :
7373194
Link To Document :
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