Name
Abstract | ||
|---|---|---|
Graphical abstractDisplay Omitted HighlightsThis paper proposes two heuristics to solve the constrained clustering problem.The two proposed methods are BRKGA with LS and CG with PR and LS heuristic.Computational results are compared with the CCCG [2], CP [8] and CPRBBA method. This paper proposes two hybrid heuristics to solve the constrained clustering problem. This problem consists of partitioning a set of objects into clusters with similar members that satisfy must-link and cannot-link constraints. A must-link constraint indicates that two selected objects must be in the same cluster, and cannot-link constraint means that two selected objects must be in distinct clusters. The two proposed hybrid methods are biased random key genetic algorithm (BRKGA) with local search (LS) heuristic and column generation (CG) with path-relinking (PR) and local search (LS) heuristic. Computational experiments considering instances available in the literature are presented to demonstrate the efficacy of the proposed methods to solve the constrained clustering problem. Moreover, the results of the CG and BRKGA are compared with the CCCG, CP and CPRBBA method. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1016/j.asoc.2017.01.023 | Appl. Soft Comput. |
Keywords | Field | DocType |
Column generation,BRKGA,Clustering | Cluster (physics),Column generation,Heuristics,Artificial intelligence,Cluster analysis,Genetic algorithm,Mathematical optimization,Heuristic,Algorithm,Constrained clustering,Local search (optimization),Mathematics,Machine learning | Journal |
Volume | Issue | ISSN |
54 | C | 1568-4946 |
Citations | PageRank | References |
1 | 0.34 | 21 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rudinei Martins de Oliveira | 1 | 31 | 2.06 |
| Antonio Augusto Chaves | 2 | 116 | 10.24 |
| Luiz Antonio Nogueira Lorena | 3 | 498 | 36.72 |