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A fuzzy self‐organizing map algorithm for biological pattern recognition
Author(s) -
Karabulut Mustafa,
İbrikci Turgay
Publication year - 2012
Publication title -
expert systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.365
H-Index - 38
eISSN - 1468-0394
pISSN - 0266-4720
DOI - 10.1111/j.1468-0394.2010.00560.x
Subject(s) - computer science , cluster analysis , self organizing map , pattern recognition (psychology) , task (project management) , fuzzy logic , key (lock) , artificial intelligence , data mining , fuzzy clustering , algorithm , computer security , management , economics
Data clustering is a key task for various processes including sequence analysis and pattern recognition. This paper studies a clustering algorithm that aimed to increase accuracy and sensitivity when working with biological data such as DNA sequences. The new algorithm is a modified version of fuzzy C‐means (FCM) and is based on the well‐known self‐organizing map (SOM). In order to show the performance of the algorithm, seven different data sets are processed. The experimental results demonstrate that the proposed algorithm has the potential to outperform SOM and FCM in terms of clustering and classification accuracy abilities. Additionally, a brief comparison is made the proposed algorithm with some previously studied ‘FCM‐SOM’ hybrid algorithms from the literature.