For Full-Text PDF, please login, if you are a member of IEICE,|
or go to Pay Per View on menu list, if you are a nonmember of IEICE.
Fuzzy c-Means Algorithms for Data with Tolerance Based on Opposite Criterions
Yuchi KANZAWA Yasunori ENDO Sadaaki MIYAMOTO
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences
Publication Date: 2007/10/01
Online ISSN: 1745-1337
Print ISSN: 0916-8508
Type of Manuscript: Special Section PAPER (Special Section on Nonlinear Theory and its Applications)
Category: Soft Computing
fuzzy c-means, clustering, tolerance, reliability of the clustering result,
Full Text: PDF>>
In this paper, two new clustering algorithms are proposed for the data with some errors. In any of these algorithms, the error is interpreted as one of decision variables -- called "tolerance" -- of a certain optimization problem like the previously proposed algorithm, but the tolerance is determined based on the opposite criterion to its corresponding previously proposed one. Applying our each algorithm together with its corresponding previously proposed one, a reliability of the clustering result is discussed. Through some numerical experiments, the validity of this paper is discussed.