Clustering problems in a multiobjective framework
保存先:
| 著者: | , |
|---|---|
| フォーマット: | artículo original |
| 状態: | Versión publicada |
| 出版日付: | 2016 |
| その他の書誌記述: | We propose a new algorithm using tabu search to deal with biobjective clustering problems. A cluster is a collection of records that are similar to one other and dissimilar to records in other clusters. Clustering has applications in VLSI design, protein-protein interaction networks, data mining and many others areas. Clustering problems have been subject of numerous studies; however, most of the work has focused on single-objective problems. In the context of multiobjective optimization our aim is to find a good approximation to the Pareto front and provide a method to make decisions. As an application problem we present the zoning problem by allowing the optimization of two objectives. |
| 国: | Portal de Revistas UCR |
| 機関: | Universidad de Costa Rica |
| Repositorio: | Portal de Revistas UCR |
| 言語: | Inglés |
| OAI Identifier: | oai:portal.ucr.ac.cr:article/25270 |
| オンライン･アクセス: | https://revistas.ucr.ac.cr/index.php/matematica/article/view/25270 |
| キーワード: | combinatorial data analysis clustering tabu search multiobjective optimization Análisis de datos combinatorio cluster búsqueda tabú optimización multiobjetivo |