General fuzzy min-max neural network for clustering and classification [PDF]
, 2000 This paper describes a general fuzzy min-max (GFMM) neural network which is a generalization and extension of the fuzzy min-max clustering and classification algorithms of Simpson (1992, 1993).
Bargiela, Andrzej, Gabrys, Bogdan
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Random Fuzzy Clustering Granular Hyperplane Classifier
IEEE Access, 2020 Granular computing is a method of studying human intelligent information processing, which has advantage of knowledge discovery. In this paper, we convert a classification problem of sample space into a classification problem of fuzzy clustering granular
Wei Li +5 more
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A new Semi-Supervised Intuitionistic Fuzzy C-means Clustering [PDF]
EAI Endorsed Transactions on Scalable Information Systems, 2020 Semi-supervised clustering algorithms aim to increase the accuracy of unsupervised clustering process by effectively exploring the limited supervision available in the form of labelled data.
J. Arora, M. Tushir
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Comparison of different strategies of utilizing fuzzy clustering in structure identification [PDF]
, 2007 Fuzzy systems approximate highly nonlinear systems by means of fuzzy "if-then" rules. In the literature, various algorithms are proposed for mining. These algorithms commonly utilize fuzzy clustering in structure identification.
Kılıç, Kemal +2 more
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, 2015 Collimated streams of particles produced in high energy physics experiments are organized using clustering algorithms to form jets. To construct jets, the experimental collaborations based at the Large Hadron Collider (LHC) primarily use agglomerative ...
Mackey, Lester +3 more
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Fuzzy Equivalence Relation Clustering Method Based on Constraint Conditions [PDF]
Jisuanji gongcheng, 2017 The traditional fuzzy equivalence relation clustering method cannot clusteraccording tospecific constraints,so that the clustering results have low accuracy,anddonot meet the requirement.In order to solve this problem,based on traditional fuzzy ...
LIANG Yuan,CHE Ming
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Fuzzy image segmentation of generic shaped clusters [PDF]
, 2005 The segmentation performance of any clustering algorithm is very sensitive to the features in an image, which ultimately restricts their generalisation capability.
Ali, M. A. +2 more
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Approximating a similarity matrix by a latent class model: A reappraisal of additive fuzzy clustering [PDF]
, 2009 Let Q be a given n×n square symmetric matrix of nonnegative elements between 0 and 1, similarities. Fuzzy clustering results in fuzzy assignment of individuals to K clusters.
Bink, M.C.A.M. +3 more
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AN APPROACH TO REMOVE THE EFFECT OF RANDOM INITIALIZATION FROM FUZZY C-MEANS CLUSTERING TECHNIQUE [PDF]
Journal of Process Management and New Technologies, 2014 Out of the different available fuzzy clustering techniques Bezdek’s Fuzzy C-Means clustering technique is among the most popular ones. Due to the random initialization of the membership values the performance of Fuzzy C-Means clustering technique ...
Samarjit Das, Hemanta K. Baruah
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A Fuzzy C-means Algorithm for Clustering Fuzzy Data and Its Application in Clustering Incomplete Data [PDF]
Journal of Artificial Intelligence and Data Mining, 2020 The fuzzy c-means clustering algorithm is a useful tool for clustering; but it is convenient only for crisp complete data. In this article, an enhancement of the algorithm is proposed which is suitable for clustering trapezoidal fuzzy data.
J. Tayyebi, E. Hosseinzadeh
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