A bee-inspired algorithm for optimal data clustering
Tipo
Artigo de evento
Data de publicação
2013
Periódico
2013 IEEE Congress on Evolutionary Computation, CEC 2013
Citações (Scopus)
15
Autores
Cruz D.P.F.
Maia R.D.
Szabo A.
De Castro L.N.
Maia R.D.
Szabo A.
De Castro L.N.
Orientador
Título da Revista
ISSN da Revista
Título de Volume
Membros da banca
Programa
Resumo
The amount of data generated in different knowledge areas has made it necessary the use of data mining tools capable of automatically analyzing and extracting knowledge from datasets. Clustering is one of the most important tasks in data mining and can be defined as the process of partitioning objects into groups or clusters, such that objects in the same group are more similar to one another than to objects belonging to other groups. In this context, this paper aims to propose an adaptation of a bee-inspired optimization algorithm so that it is able to solve data clustering problems. The algorithm was run for different datasets and the results obtained showed high quality clusters and diversity of solutions, whilst a suitable number of clusters was automatically determined. © 2013 IEEE.
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Assuntos Scopus
Bee-inspired algorithms , Data-mining tools , Diversity of solutions , Dynamic sizes , Number of clusters , Optimal data , Optimization algorithms , Swarm Intelligence