Optimality tests for the fuzzy c-means algorithm
References (7)
- et al.
Fuzzy c-means: optimality of solutions and effective termination of the algorithm
Pattern Recognition
(1986) Comments on: Optimality for fixed points by Kim, et al.
Pattern Recognition
(1990)- et al.
Optimality tests for fixed points of the fuzzy c-means algorithm
Pattern Recognition
(1988)
Cited by (37)
Fuzzy c-means clustering using Jeffreys-divergence based similarity measure
2020, Applied Soft Computing JournalImproving Wang-Mendel method performance in fuzzy rules generation using the fuzzy C-means clustering algorithm
2015, NeurocomputingCitation Excerpt :In addition, Eqs. (5) and (6) are used in step 2 of the AFCM algorithm to update the degree of affinity, which is considered to be equal in the initialization step. A number of previous studies have investigated the convergence properties of FCM clustering [28–30]. The sample-weighted algorithm is not influenced by outliers, while its superiority and effectiveness have been demonstrated [27].
Rough clustering using generalized fuzzy clustering algorithm
2013, Pattern RecognitionAn enhanced classification method comprising a genetic algorithm, rough set theory and a modified PBMF-index function
2012, Applied Soft Computing JournalCitation Excerpt :Amongst these methods, traditional indices such as the partition coefficient [6,7] and classification entropy coefficient [8,9] are based simply on the membership values of the items within the dataset and are therefore computationally straightforward. However, recent studies have shown that the accuracy of a cluster validity index can be improved by considering not only the dataset itself, but also the matrix U used to partition the data [10–17]. In general, existing clustering methods cluster the dataset in accordance with the norms of the instances rather than the values of the individual attributes of the instances.
Sample-weighted clustering methods
2011, Computers and Mathematics with Applications