DocumentCode
441998
Title
Clustering of gene expression data based on self-growth tree
Author
Zhang, Shi-Wei ; Lin, Lei ; Guan, Yi ; Wang, Xiao-long
Author_Institution
Sch. of Comput. Sci. & Technol., Harbin Inst. of Technol., China
Volume
6
fYear
2005
fDate
18-21 Aug. 2005
Firstpage
3377
Abstract
A novel approach used in gene expression data is proposed in this paper. Unlike traditional hierarchical clustering, this method has a lower computational complexity and more rational tree structure, and compared with K-means method, it is influenced less by people. It is also applied in the cell cycle data set reported by Cho, and obtains some good results.
Keywords
biology computing; computational complexity; data mining; genetics; pattern clustering; tree data structures; K-means method; cell cycle data set; computational complexity; gene expression data; self-growth tree structure; Binary trees; Clustering algorithms; Computational complexity; Computer science; Data analysis; Distributed computing; Flowcharts; Gene expression; Humans; Tree data structures; Gene expression data; K-means; clustering; hierarchical clustering;
fLanguage
English
Publisher
ieee
Conference_Titel
Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on
Conference_Location
Guangzhou, China
Print_ISBN
0-7803-9091-1
Type
conf
DOI
10.1109/ICMLC.2005.1527525
Filename
1527525
Link To Document