DocumentCode :
460877
Title :
QTOP-K: A novel Algorithm for mining high quality pattern-based clusters in GST Microarray Data
Author :
Chen, Shuhui ; Tang, Zhonghua ; Chen, Bo ; Fu, Hongzhuo ; Hao, Zhifeng
Author_Institution :
Sch. of Math. Sci., South China Univ. of Technol.
Volume :
1
fYear :
2006
fDate :
Nov. 2006
Firstpage :
828
Lastpage :
831
Abstract :
Pattern-based clustering is widely applied in bioinformatics and biomedical Recently, mining high quality pattern-based clusters has become an important research direction. However, the existing methods were neither efficient in large data set nor precise at measuring the quality of clusters. These problems have greatly limited the methods´ application in large data set. This paper proposes a new algorithm, which can provide a more accurate measurement for the quality of clusters and sharply cut down the time for mining high quality patterned-based clusters compared with today´s methods. Experiments are held on real data set and synthetic data set and the test result suggests that Qtop-k has made notable progress in the aforementioned problems
Keywords :
data mining; pattern clustering; GST microarray data; QTOP-K; pattern clustering; pattern mining; Bioinformatics; Biomedical engineering; Biomedical measurements; Clustering algorithms; Computer science; Costs; Data engineering; Software algorithms; Software quality; Time measurement;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Intelligence and Security, 2006 International Conference on
Conference_Location :
Guangzhou
Print_ISBN :
1-4244-0605-6
Electronic_ISBN :
1-4244-0605-6
Type :
conf
DOI :
10.1109/ICCIAS.2006.294252
Filename :
4072205
Link To Document :
بازگشت