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A threshold free clustering algorithm for robust unsupervised classification

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dc.contributor.authorTemei, Turgay
dc.contributor.authorAydın, Nizamettin
dc.contributor.institutionTemei, Turgay, Department of Electrical Engineering, Fatih Üniversitesi, Istanbul, Turkey
dc.contributor.institutionAydın, Nizamettin, Engineering Faculty, Bahçeşehir Üniversitesi, Istanbul, Turkey
dc.date.accessioned2025-10-05T16:50:13Z
dc.date.issued2007
dc.description.abstractA new information-theoretic, unsupervised, subtractive clustering algorithm is proposed. The algorithm eliminates threshold constraint to detect possible cluster members. Cluster centers are formed with minimum entropy. Instead of using a fixedthreshold, a decision region is formed with the use of maximum mutual information. Cluster members are chosen with a relative-cost assigned in partitions of data set. The algorithm yields more reliably distributed cluster numbers in statistical sense, hence reducing further computation for validation, which is justified for a set of synthetic data. © 2007 IEEE. © 2008 Elsevier B.V., All rights reserved.
dc.identifier.conferenceName2007 ECSIS Symposium on Bio-inspired, Learning, and Intelligent Systems for Security, BLISS 2007
dc.identifier.conferencePlaceEdinburgh
dc.identifier.doi10.1109/BLISS.2007.7
dc.identifier.endpage122
dc.identifier.isbn0769529194
dc.identifier.isbn9780769529196
dc.identifier.scopus2-s2.0-46449085511
dc.identifier.startpage119
dc.identifier.urihttps://doi.org/10.1109/BLISS.2007.7
dc.identifier.urihttps://hdl.handle.net/20.500.14719/13926
dc.language.isoen
dc.subject.authorkeywordsBoolean Functions
dc.subject.authorkeywordsCluster Analysis
dc.subject.authorkeywordsFeature Extraction
dc.subject.authorkeywordsFlow Of Solids
dc.subject.authorkeywordsInformation Theory
dc.subject.authorkeywordsIntelligent Control
dc.subject.authorkeywordsIntelligent Systems
dc.subject.authorkeywordsSet Theory
dc.subject.authorkeywordsStereophonic Broadcasting
dc.subject.authorkeywordsBio-inspired
dc.subject.authorkeywordsCluster Centers
dc.subject.authorkeywordsCluster Members
dc.subject.authorkeywordsCluster Numbers
dc.subject.authorkeywordsData Sets
dc.subject.authorkeywordsMaximum Mutual Information (mmi)
dc.subject.authorkeywordsMinimum Entropy
dc.subject.authorkeywordsSubtractive Clustering
dc.subject.authorkeywordsSynthetic Data
dc.subject.authorkeywordsUnsupervised Classification
dc.subject.authorkeywordsClustering Algorithms
dc.subject.indexkeywordsBoolean functions
dc.subject.indexkeywordsCluster analysis
dc.subject.indexkeywordsFeature extraction
dc.subject.indexkeywordsFlow of solids
dc.subject.indexkeywordsInformation theory
dc.subject.indexkeywordsIntelligent control
dc.subject.indexkeywordsIntelligent systems
dc.subject.indexkeywordsSet theory
dc.subject.indexkeywordsStereophonic broadcasting
dc.subject.indexkeywordsBio-inspired
dc.subject.indexkeywordsCluster centers
dc.subject.indexkeywordsCluster members
dc.subject.indexkeywordscluster numbers
dc.subject.indexkeywordsData sets
dc.subject.indexkeywordsMaximum mutual information (MMI)
dc.subject.indexkeywordsMinimum entropy
dc.subject.indexkeywordsSubtractive clustering
dc.subject.indexkeywordsSynthetic data
dc.subject.indexkeywordsUnsupervised classification
dc.subject.indexkeywordsClustering algorithms
dc.titleA threshold free clustering algorithm for robust unsupervised classification
dc.typeConference Paper
dcterms.referencesFigueiredo, Mário A.T., Unsupervised learning of finite mixture models, IEEE Transactions on Pattern Analysis and Machine Intelligence, 24, 3, pp. 381-396, (2002), How Many Clusters Which Clustering Method Answers Via Model Based Cluster Analysis, (1998), SIGKDD Explorations, (2004), Agrawal, Rakesh, Automatic subspace clustering of high dimensional data for data mining applications, SIGMOD Record, 27, 2, pp. 94-105, (1998), Gokçay, Erhan, Information theoretic clustering, IEEE Transactions on Pattern Analysis and Machine Intelligence, 24, 2, pp. 158-171, (2002), Machine Learning Proceedings of the Thirteenth International Conference Icml 96, (1996), Veenman, Cor J., A maximum variance cluster algorithm, IEEE Transactions on Pattern Analysis and Machine Intelligence, 24, 9, pp. 1273-1280, (2002), Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, (1967), Yager, Ronald R., Generation of fuzzy rules by mountain clustering, Journal of Intelligent and Fuzzy Systems, 2, 3, pp. 209-219, (1994), Chiu, Stephen L., Fuzzy model identification based on cluster estimation, Journal of Intelligent and Fuzzy Systems, 2, 3, pp. 267-278, (1994)
dspace.entity.typePublication
local.indexed.atScopus
person.identifier.scopus-author-id24450982900
person.identifier.scopus-author-id7005593269

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