Paper Info
Title
A string grammar possibilistic-fuzzy C-medians
Abstract
In the context of syntactic pattern recognition, we adopt the fuzzy clustering approach to classify the syntactic pattern. A syntactic pattern can be described using a string grammar. Fuzzy clustering has been shown to have better performance than hard clustering. Previously, to improve the string grammar hard C-means, we introduced a string grammar fuzzy C-medians and string grammar fuzzy-possibilistic C-medians algorithm. However, both algorithms have their own problem. Thus, in this paper, we develop a string grammar possibilistic-fuzzy C-medians algorithm. The experiments on four real data sets show that string grammar possibilistic-fuzzy C-medians has better performance than string grammar hard C-means, string grammar fuzzy C-medians, and string grammar fuzzy-possibilistic C-medians. We claim that the proposed string grammar possibilistic-fuzzy C-medians is better than the other string grammar clustering algorithms.
Year
DOI
Venue
2019
10.1007/s00500-018-3392-6
soft computing
Keywords
Field
DocType
Fuzzy median, String grammar possibilistic-fuzzy c-medians, Levenshtein distance, Syntactic pattern recognition
Fuzzy clustering,Data set,Computer science,Fuzzy logic,Levenshtein distance,Theoretical computer science,String grammar,Syntactic pattern recognition,Cluster analysis,Syntax
Journal
Volume
Issue
ISSN
23.0
17.0
1433-7479
Citations 
PageRank 
References 
0
0.34
23
Authors
3
Name
Order
Citations
PageRank
Atcharin Klomsae152.07
S. Auephanwiriyakul224639.45
Nipon Theera-umpon318430.59