Clique Partitioning for Clustering: A Comparison withK-Means and Latent Class Analysis

DOI10.1080/03610910701723559zbMath1139.62032OpenAlexW2006239936MaRDI QIDQ5451111

Tom Obremski, Bahram Alidaee, Gary A. Kochenberger, Haibo Wang

Publication date: 18 March 2008

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610910701723559

zbMATH Keywords

metaheuristicstabu searchlatent class modelingsilhouette coefficients

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Pattern recognition, speech recognition (68T10)

Related Items (4)

Multi-attribute community detection in international trade network ⋮ Solving the clique partitioning problem as a maximally diverse grouping problem ⋮ Food market segmentation based on consumer preferences using outranking multicriteria approaches ⋮ Clustering qualitative data based on binary equivalence relations: neighborhood search heuristics for the clique partitioning problem

Cites Work

• A cutting plane algorithm for a clustering problem
• Cliques and clustering: A combinatorial approach
• A unified modeling and solution framework for combinatorial optimization problems
• Clustering of microarray data via clique partitioning
• Adaptive Memory Tabu Search for Binary Quadratic Programs
• Methods of Nonlinear 0-1 Programming
• Finding Groups in Data
• How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis
• The clique partitioning problem: Facets and patching facets
• Handbook of metaheuristics

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