Maximum cut‐clique problem: ILS heuristics and a data analysis application
This paper focuses on iterated local search heuristics for the maximum cut‐clique (MCC, or clique neighborhood) problem. Given an undirected graph G = (V,E) and a clique C of G, the cut‐clique is the set of edges running between C and V\C, establishing the cut (C,V\C). The MCC in G is to find a cliq...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2014 |
| País: | España |
| Institución: | Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
| Repositorio: | Recercat. Dipósit de la Recerca de Catalunya |
| OAI Identifier: | oai:recercat.cat:10230/44396 |
| Acceso en línea: | http://hdl.handle.net/10230/44396 http://dx.doi.org/10.1111/itor.12120 |
| Access Level: | acceso abierto |
| Palabra clave: | Cut-cliques Clique’s edge neighborhood Iterated local search heuristics Discretized formulations Market basket analysis Data mining |
| Sumario: | This paper focuses on iterated local search heuristics for the maximum cut‐clique (MCC, or clique neighborhood) problem. Given an undirected graph G = (V,E) and a clique C of G, the cut‐clique is the set of edges running between C and V\C, establishing the cut (C,V\C). The MCC in G is to find a clique with the largest number of edges in the neighborhood of the clique, also known as the maximum edge‐neighborhood clique. This problem has been recently introduced in the literature together with a number of applications, namely, in cell biology instances. However, it has only been addressed so far by exact methods. In this paper, we introduce the first approximate algorithms for tackling the MCC problem, compare the results with the exact methodologies, and explore a new application within marketing analysis, which provide a new alternative perspective for mining market basket problems. |
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