Paper Info
Title
Heuristic Approaches for the Quartet Method of Hierarchical Clustering
Abstract
Given a set of objects and their pairwise distances, we wish to determine a visual representation of the data. We use the quartet paradigm to compute a hierarchy of clusters of the objects. The method is based on an NP-hard graph optimization problem called the Minimum Quartet Tree Cost problem. This paper presents and compares several heuristic approaches to approximate the optimal hierarchy. The performance of the algorithms is tested through extensive computational experiments and it is shown that the Reduced Variable Neighborhood Search heuristic is the most effective approach to the problem, obtaining high-quality solutions in short computational running times.
Year
DOI
Venue
2010
10.1109/TKDE.2009.188
IEEE Trans. Knowl. Data Eng.
Keywords
Field
DocType
hierarchical clustering,short computational,minimum quartet tree cost,effective approach,np-hard graph optimization problem,pairwise distance,reduced variable neighborhood search,high-quality solution,heuristic approach,optimal hierarchy,heuristic approaches,extensive computational experiment,quartet method,computer experiment,artificial intelligence,networks,polynomials,data visualisation,heuristics,statistical analysis,graphs,biology,working paper,data mining,tree data structures,binary trees,topology,computational complexity,bioinformatics,optimization,clustering,simulated annealing,optimization problem,cluster analysis
Simulated annealing,Hierarchical clustering,Heuristic,Variable neighborhood search,Computer science,Tree (data structure),Algorithm,Binary tree,Artificial intelligence,Cluster analysis,Machine learning,Computational complexity theory
Journal
Volume
Issue
ISSN
22
10
1041-4347
Citations 
PageRank 
References 
8
0.54
10
Authors
5
Name
Order
Citations
PageRank
Sergio Consoli116424.96
Kenneth Darby-Dowman2523.47
Gijs Geleijnse317415.55
Jan Korst417519.94
Steffen Pauws528932.61