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
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;
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
DOI :
10.1109/ASONAM.2012.187
