Silhouette-based clustering using an immune network

dc.contributor.authorBorges E.
dc.contributor.authorFerrari D.G.
dc.contributor.authorDe Castro L.N.
dc.date.accessioned2024-03-13T01:07:10Z
dc.date.available2024-03-13T01:07:10Z
dc.date.issued2012
dc.description.abstractClustering is an important Data Mining task from the field of Knowledge Discovery in Databases. Many algorithms can perform clustering in a simple and efficient manner, but have drawbacks, such as the lack of a way to automatically determine the optimal number of clusters in the dataset and the possibility of getting stuck in local optima solutions. To try and reduce these drawbacks this work proposes a new clustering algorithm based on Artificial Immune Systems. This algorithm is characterized by the generation of multiple simultaneous high quality solutions in terms of the number of clusters in the database and the use of a cost function that explicitly evaluates the quality of clusters, minimizing the inconvenience of getting stuck in local optima solutions. © 2012 IEEE.
dc.identifier.doi10.1109/CEC.2012.6252945
dc.identifier.urihttps://dspace.mackenzie.br/handle/10899/36757
dc.relation.ispartof2012 IEEE Congress on Evolutionary Computation, CEC 2012
dc.rightsAcesso Restrito
dc.subject.otherlanguageArtificial Immune Systems
dc.subject.otherlanguageClustering
dc.subject.otherlanguageDiversity
dc.subject.otherlanguageEvolutionary Algorithms
dc.subject.otherlanguageK-means
dc.titleSilhouette-based clustering using an immune network
dc.typeArtigo de evento
local.scopus.citations6
local.scopus.eid2-s2.0-84866862465
local.scopus.subjectArtificial Immune System
local.scopus.subjectClustering
local.scopus.subjectData mining tasks
local.scopus.subjectData sets
local.scopus.subjectDiversity
local.scopus.subjectHigh-quality solutions
local.scopus.subjectImmune network
local.scopus.subjectK-means
local.scopus.subjectKnowledge discovery in database
local.scopus.subjectLocal optima
local.scopus.subjectNumber of clusters
local.scopus.subjectOptimal number
local.scopus.updated2024-05-01
local.scopus.urlhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84866862465&origin=inward
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