A bee-inspired algorithm for optimal data clustering

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 objec...

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Bibliographic Details
Published in2013 IEEE Congress on Evolutionary Computation pp. 3140 - 3147
Main Authors Ferreira Cruz, Davila Patricia, Dourado Maia, Renato, Szabo, Alexandre, Nunes de Castro, Leandro
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2013
Subjects
Algorithm design and analysis
bee-inspired algorithms
Clustering algorithms
Computers
Data mining
dynamic size population
Entropy
optimal data clustering
Optimization
Sociology
swarm intelligence
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Summary: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.
ISBN:1479904538
9781479904532
ISSN:1089-778X
1941-0026
DOI:10.1109/CEC.2013.6557953