A SINGLE-STEP CLUSTERING ALGORITHM BASED ON A NEW INFORMATION-THEORETIC SAMPLE ASSOCIATION METRIC DEFINITION
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Dosyalar
Tarih
2017
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Acad Sciences Czech Republic, Inst Computer Science
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
A single-step information-theoretic algorithm that is able to identify possible clusters in dataset is presented. The proposed algorithm consists in representation of data scatter in terms of similarity-based data point entropy and probability descriptions. By using these quantities, an information-theoretic association metric called mutual ambiguity between data points is defined, which then is to be employed in determining particular data points called cluster identifiers. For forming individual clusters corresponding to cluster identifiers determined as such, a cluster relevance rule is defined. Since cluster identifiers and associative cluster member data points can be identified without recursive or iterative search, the algorithm is single-step. The algorithm is tested and justified with experiments by using synthetic and anonymous real datasets. Simulation results demonstrate that the proposed algorithm also exhibits more reliable performance in statistical sense compared to major algorithms.
Açıklama
Anahtar Kelimeler
clustering, clustering algorithms, information theory, mutual information, unsupervised learning
Kaynak
Neural Network World
WoS Q DeÄŸeri
Q4
Scopus Q DeÄŸeri
Q4
Cilt
27
Sayı
5
