Paper Info
Title
An Iterative Algorithm for Approximate Median Graph Computation
Abstract
Recently, the median graph has been shown to be a good choice to obtain a representative of a given set of graphs. It has been successfully applied to graph-based classification and clustering. In this paper we exploit a theoretical property of the median, which has not yet been utilized in the past, to derive a new iterative algorithm for approximate median graph computation. Experiments done using five different graph databases show that the proposed approach yields, in four out of these five datasets, better medians than two of the previous existing methods.
Year
DOI
Venue
2010
10.1109/ICPR.2010.386
Pattern Recognition
Keywords
Field
DocType
graph theory,iterative methods,pattern classification,pattern clustering,approximate median graph computation,graph databases,graph-based classification,graph-based clustering,iterative algorithm
Graph theory,Approximation algorithm,Graph database,Pattern recognition,Iterative method,Computer science,Graph bandwidth,Artificial intelligence,Cluster analysis,Median graph,Computation
Conference
ISSN
ISBN
Citations 
1051-4651
978-1-4244-7542-1
5
PageRank 
References 
Authors
0.42
10
2
Name
Order
Citations
PageRank
Miquel Ferrer122013.30
Horst Bunke28650558.39