Journal ArticleDOI
On a class of fuzzy classification maximum likelihood procedures
TLDR
The fuzzy extension of the CML procedure is made, which extends the fuzzy clustering algorithms of Trauwaert, Kaufman and Rousseeuw by adding a penalty term, and finds that the penalized FCM is more meaningful and effective than FCM.About:
This article is published in Fuzzy Sets and Systems.The article was published on 1993-08-01. It has received 111 citations till now. The article focuses on the topics: Fuzzy clustering & Fuzzy classification.read more
Citations
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Journal ArticleDOI
A survey of fuzzy clustering
TL;DR: A survey of fuzzy set theory applied in cluster analysis in three categories: the fuzzy clustering based on fuzzy relation, the fuzzy generalized k-nearest neighbor rule, and an overview of a nonparametric classifier.
Journal ArticleDOI
Image segmentation by generalized hierarchical fuzzy C-means algorithm
TL;DR: This paper introduces a new generalized hierarchical FCM (GHFCM), which is more robust to image noise with the spatial constraints: the generalized mean, and introduces a more flexibility function which considers the distance function itself as a sub-FCM.
Journal ArticleDOI
Clustering: A neural network approach
TL;DR: A comprehensive overview of competitive learning based clustering methods is given and two examples are given to demonstrate the use of the clustering Methods.
Journal ArticleDOI
On a class of fuzzy c -numbers clustering procedures for fuzzy data
Miin-Shen Yang,Chen-Hsiu Ko +1 more
TL;DR: New types of fuzzy clustering procedures in dealing with fuzzy data are derived, called fuzzy c-numbers (FCN) clusterings, which construct these FCNs for U-type, triangular, trapezoidal and normal fuzzy numbers.
Journal ArticleDOI
A Gaussian kernel-based fuzzy c-means algorithm with a spatial bias correction
Miin-Shen Yang,Hsu-Shen Tsai +1 more
TL;DR: This paper presents a Gaussian kernel-based fuzzy c-means algorithm (GKFCM) with a spatial bias correction that becomes a generalized type of FCM, BCFCM, KFCM_S"1 and KFCS"2 algorithms and presents with more efficiency and robustness.
References
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Book
Pattern Recognition with Fuzzy Objective Function Algorithms
TL;DR: Books, as a source that may involve the facts, opinion, literature, religion, and many others are the great friends to join with, becomes what you need to get.
Journal ArticleDOI
A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters
TL;DR: Two fuzzy versions of the k-means optimal, least squared error partitioning problem are formulated for finite subsets X of a general inner product space; in both cases, the extremizing solutions are shown to be fixed points of a certain operator T on the class of fuzzy, k-partitions of X, and simple iteration of T provides an algorithm which has the descent property relative to the least squarederror criterion function.
A fuzzy relative of the isodata process and its use in detecting compact well-separated clusters
TL;DR: In this paper, two fuzzy versions of the k-means optimal, least squared error partitioning problem are formulated for finite subsets X of a general inner product space, and the extremizing solutions are shown to be fixed points of a certain operator T on the class of fuzzy, k-partitions of X, and simple iteration of T provides an algorithm which has the descent property relative to the LSE criterion function.
Book
Mixture models : inference and applications to clustering
TL;DR: The Mixture Likelihood Approach to Clustering and the Case Study Homogeneity of Mixing Proportions Assessing the Performance of the Mixture likelihood approach toClustering.