Optimal Clustering Selection on Hierarchical System Network

Authors:
Eddie Fuller;Wenliang Tang;Yezhou Wu;Cun-Quan Zhang
Affiliations:
-;-;-;-
Venue:
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Year:
2012

Citing 3
Cited 0

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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.