The \(k\)-means range algorithm for personalized data clustering in e-commerce

DOI10.1016/j.ejor.2005.04.011zbMath1110.90054OpenAlexW2119165863MaRDI QIDQ857263

Georgios P. Papamichail, Dimitrios P. Papamichail

Publication date: 14 December 2006

Published in: European Journal of Operational Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ejor.2005.04.011

zbMATH Keywords

heuristicsdata clusteringrange searchdistributed consumer decision-makingpersonalized systems

Mathematics Subject Classification ID

Management decision making, including multiple objectives (90B50) Approximation methods and heuristics in mathematical programming (90C59)

Related Items

Recommendation generation using personalized weight of meta-paths in heterogeneous information networks ⋮ Handling multicriteria preferences in cluster analysis

Uses Software

• clusfind

Cites Work

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• K-Means-Type Algorithms: A Generalized Convergence Theorem and Characterization of Local Optimality
• New Data Structures for Orthogonal Range Queries
• Finding Groups in Data

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