Heuristic Approaches for the Quartet Method of Hierarchical Clustering

• Published in: IEEE transactions on knowledge and data engineering Vol. 22; no. 10; pp. 1428 - 1443
• Main Authors: Consoli, S, Darby-Dowman, K, Geleijnse, G, Korst, J, Pauws, S
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.10.2010; IEEE Computer Society; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Applied sciences; Approximation; Binary trees; Biology computing; Clustering; Clustering algorithms; Clustering methods; Computation; Computer science; control theory; systems; Cost function; Data processing. List processing. Character string processing; Exact sciences and technology; graphs and networks; Heuristic methods; Hierarchies; High performance computing; Memory organisation. Data processing; Optimization; Optimization methods; Representations; Running; Software; Symmetric matrices; Testing; Tree graphs; Visual; Hierarchical classification; Dynamic analysis; Heuristic method; NP hard problem; heuristic methods; Unsupervised classification; Data mining; Clustering; graphs and networks; Optimization
• ISSN: 1041-4347; 1558-2191
• DOI: 10.1109/TKDE.2009.188

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.