DocumentCode
3155537
Title
Optimal Clustering Selection on Hierarchical System Network
Author
Fuller, E. ; Wenliang Tang ; Yezhou Wu ; Cun-Quan Zhang
Author_Institution
Dept. of Math., West Virginia Univ., Morgantown, WV, USA
fYear
2012
fDate
26-29 Aug. 2012
Firstpage
1085
Lastpage
1089
Abstract
In data mining, hierarchical clustering is a method of cluster analysis which seeks to build a hierarchy of clusters. Strategies for hierarchical clustering generally fall into two types: agglomerative and divisive. In this paper we shall introduce a new optimal selection method based on the well-known Max-Flow Min-Cut theorem, which also works for the hierarchically structure with overlapping. A novel dynamic algorithm was presented for the special structure without overlapping.
Keywords
data mining; minimax techniques; pattern clustering; agglomerative clustering; cluster analysis; data mining; divisive clustering; dynamic algorithm; hierarchical clustering; hierarchical system network; hierarchically structure; max-flow min-cut theorem; optimal clustering selection; Algorithm design and analysis; Clustering algorithms; Communities; Educational institutions; Heuristic algorithms; Synthetic aperture sonar; USA Councils; Hierarchical Clustering; Max-Flow Min-Cut Theorem;
fLanguage
English
Publisher
ieee
Conference_Titel
Advances in Social Networks Analysis and Mining (ASONAM), 2012 IEEE/ACM International Conference on
Conference_Location
Istanbul
Print_ISBN
978-1-4673-2497-7
Type
conf
DOI
10.1109/ASONAM.2012.187
Filename
6425614
Link To Document