A graph partitioning approach to SOM clustering

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dc.contributor.authorSilva L.A.
dc.contributor.authorCosta J.A.F.
dc.date.accessioned2024-03-13T01:10:31Z
dc.date.available2024-03-13T01:10:31Z
dc.date.issued2011
dc.description.abstractDetermining the number of clusters has been one of the most difficult problems in data clustering. The Self-Organizing Map (SOM) has been widely used for data visualization and clustering. The SOM can reduce the complexity in terms of computation and noise of input patterns. However, post processing steps are needed to extract the real data structure learnt by the map. One approach is to use other algorithm, such as K-means, to cluster neurons. Finding the best value of K can be aided by using an cluster validity index. On the other hand, graph-based clustering has been used for cluster analysis. This paper addresses an alternative methodology using graph theory for SOM clustering. The Davies-Bouldin index is used as a cluster validity to analyze inconsistent neighboring relations between neurons. The result is a segmented map, which indicates the number of clusters as well as the labeled neurons. This approach is compared with the traditional approach using K-means. The experimental results using the approach addressed here with three different databases presented consistent results of the expected number of clusters. © 2011 Springer-Verlag.
dc.description.firstpage152
dc.description.lastpage159
dc.description.volume6936 LNCS
dc.identifier.doi10.1007/978-3-642-23878-9_19
dc.identifier.issn0302-9743
dc.identifier.urihttps://dspace.mackenzie.br/handle/10899/36945
dc.relation.ispartofLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.rightsAcesso Restrito
dc.subject.otherlanguageData clustering
dc.subject.otherlanguagegraph theory
dc.subject.otherlanguageK-Means
dc.subject.otherlanguageSelf-Organizing Map
dc.titleA graph partitioning approach to SOM clustering
dc.typeArtigo de evento
local.scopus.citations0
local.scopus.eid2-s2.0-80053025802
local.scopus.subjectBest value
local.scopus.subjectCluster validity
local.scopus.subjectCluster validity indices
local.scopus.subjectData clustering
local.scopus.subjectDavies-Bouldin index
local.scopus.subjectDetermining the number of clusters
local.scopus.subjectGraph Partitioning
local.scopus.subjectGraph-based clustering
local.scopus.subjectInput patterns
local.scopus.subjectK-Means
local.scopus.subjectNumber of clusters
local.scopus.subjectPost processing
local.scopus.subjectSelf organizing
local.scopus.subjectSOM clustering
local.scopus.subjectCluster validity
local.scopus.subjectCluster validity indices
local.scopus.subjectData clustering
local.scopus.subjectDetermining the number of clusters
local.scopus.subjectGraph Partitioning
local.scopus.subjectK-means
local.scopus.subjectNumber of clusters
local.scopus.subjectTraditional approaches
local.scopus.updated2024-05-01
local.scopus.urlhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=80053025802&origin=inward
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