Improved Fuzzy Art Method for Initializing K-means
Author(s) -
Sevinç İlhan Omurca,
N. Jeremi Duru,
Eşref Adalı
Publication year - 2010
Publication title -
international journal of computational intelligence systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.385
H-Index - 41
eISSN - 1875-6891
pISSN - 1875-6883
DOI - 10.1080/18756891.2010.9727698
Subject(s) - initialization , fuzzy logic , cluster analysis , computer science , margin (machine learning) , fuzzy clustering , data mining , cluster (spacecraft) , scope (computer science) , artificial intelligence , pattern recognition (psychology) , mathematics , machine learning , programming language
The K-means algorithm is quite sensitive to the cluster centers selected initially and can perform different clusterings depending on these initialization conditions. Within the scope of this study, a new method based on the Fuzzy ART algorithm which is called Improved Fuzzy ART (IFART) is used in the determination of initial cluster centers. By using IFART, better quality clusters are achieved than Fuzzy ART do and also IFART is as good as Fuzzy ART about capable of fast clustering and capability on large scaled data clustering. Consequently, it is observed that, with the proposed method, the clustering operation is completed in fewer steps, that it is performed in a more stable manner by fixing the initialization points and that it is completed with a smaller error margin compared with the conventional K-means.
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