DocumentCode :
2243305
Title :
Implementation of rough fuzzy k-means clustering algorithm in Matlab
Author :
Zhang, Jun-hao ; Ha, Ming-Hu ; Wu, Jing
Author_Institution :
Coll. of Math. & Comput. Sci., Hebei Univ., Baoding, China
Volume :
4
fYear :
2010
fDate :
11-14 July 2010
Firstpage :
2084
Lastpage :
2087
Abstract :
With the assistance of the lower and upper approximation of rough sets, the rough fuzzy k-means clustering algorithm may improve the objective function and further the distribution of membership function for the traditional fuzzy k-means clustering. However, the algorithm only has theoretical ideas rather than concrete realizations. To make it better applied to practice, using Matlab, a mathematical programming tool, to implement rough fuzzy k-means clustering algorithm is discussed. Moreover, steps of implementation are given in detail. The foresaid contributions may provide clustering learners and non-computer professional researchers with a simple, convenient, efficient and feasible implementation method.
Keywords :
fuzzy set theory; mathematical programming; mathematics computing; matrix algebra; pattern clustering; rough set theory; Matlab; mathematical programming tool; membership function; rough fuzzy k-means clustering algorithm; rough sets; Algorithm design and analysis; Approximation algorithms; Approximation methods; Classification algorithms; Clustering algorithms; Machine learning; Presses; Fuzzy k-means; Matlab; Rough fuzzy k-means;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning and Cybernetics (ICMLC), 2010 International Conference on
Conference_Location :
Qingdao
Print_ISBN :
978-1-4244-6526-2
Type :
conf
DOI :
10.1109/ICMLC.2010.5580498
Filename :
5580498
Link To Document :
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