DILS: constrained clustering through dual iterative local search

DOI10.1016/j.cor.2020.104979zbMath1458.68202OpenAlexW3020413602MaRDI QIDQ2664309

Germán González-Almagro, Salvador Garcia, Julián Luengo, José Ramón Cano

Publication date: 20 April 2021

Published in: Computers \& Operations Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.cor.2020.104979

zbMATH Keywords

constrained clusteringcannot-linkdual iterative local searchinstance-levelmust-link

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Approximation methods and heuristics in mathematical programming (90C59) Applications of stochastic analysis (to PDEs, etc.) (60H30) Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) (68T20)

Related Items (5)

Homogeneous grouping of non-prime steel products for online auctions: a case study ⋮ Neighborhood decomposition-driven variable neighborhood search for capacitated clustering ⋮ An exact algorithm for semi-supervised minimum sum-of-squares clustering ⋮ Semi-supervised \(k\)-means clustering via DC programming approach ⋮ DILS

Uses Software

• CECM
• KEEL
• Scikit
• rNPBST

Cites Work

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• Structural, Syntactic, and Statistical Pattern Recognition
• Handbook of metaheuristics
• Biased‐randomized iterated local search for a multiperiod vehicle routing problem with price discounts for delivery flexibility

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