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Clustering problems in a multiobjective framework
Author(s) -
Yunay Hernández,
Ricardo P. Beausoleil
Publication year - 2016
Publication title -
revista de matemáticas
Language(s) - English
Resource type - Journals
eISSN - 2215-3373
pISSN - 1409-2433
DOI - 10.15517/rmta.v23i2.25270
Subject(s) - cluster analysis , multi objective optimization , computer science , context (archaeology) , data mining , tabu search , pareto principle , mathematical optimization , machine learning , artificial intelligence , mathematics , geography , archaeology
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 nd 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.

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