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TitleA simple clustering algorithm based on weighted expected distances
Author(s)Rocha, Ana Maria A. C.
Costa, M. Fernanda P.
Fernandes, Edite Manuela da G. P.
KeywordsClustering analysis
Partitioning algorithms
Weighted distance
Issue date2021
PublisherSpringer Nature
JournalCommunications in Computer and Information Science
CitationRocha A.M.A.C., Costa M.F.P., Fernandes E.M.G.P. (2021) A Simple Clustering Algorithm Based on Weighted Expected Distances. In: Pereira A.I. et al. (eds) Optimization, Learning Algorithms and Applications. OL2A 2021. Communications in Computer and Information Science, vol 1488. Springer, Cham.
Abstract(s)This paper contains a proposal to assign points to clusters, represented by their centers, based on weighted expected distances in a cluster analysis context. The proposed clustering algorithm has mechanisms to create new clusters, to merge two nearby clusters and remove very small clusters, and to identify points ‘noise’ when they are beyond a reasonable neighborhood of a center or belong to a cluster with very few points. The presented clustering algorithm is evaluated using four randomly generated and two well-known data sets. The obtained clustering is compared to other clustering algorithms through the visualization of the clustering, the value of the DB validity measure and the value of the sum of within-cluster distances. The preliminary comparison of results shows that the proposed clustering algorithm is very efficient and effective.
TypeConference paper
Publisher version
AccessOpen access
Appears in Collections:CAlg - Artigos em livros de atas/Papers in proceedings
CMAT - Artigos em atas de conferências e capítulos de livros com arbitragem / Papers in proceedings of conferences and book chapters with peer review

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