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Fuzzy C-means++: Fuzzy C-means with effective seeding initialization [PDF]
Expert Systems With Applications, 2015 AbstractFuzzy C-means has been utilized successfully in a wide range of applications, extending the clustering capability of the K-means to datasets that are uncertain, vague and otherwise hard to cluster. This paper introduces the Fuzzy C-means++ algorithm which, by utilizing the seeding mechanism of the K-means++ algorithm, improves the effectiveness
Xiao-Jun Zeng, Adrian Stetco
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Pattern Recognition Letters, 1996
A Fuzzy C-Means-based clustering method guided by an auxiliary (conditional) variable is introduced. The method reveals a structure within a family of patterns by considering their vicinity in a feature space along with the similarity of the values assumed by a certain conditional variable. The usefulness of the algorithm is exemplified in the problems
Witold Pedrycz
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A Fuzzy C-Means-based clustering method guided by an auxiliary (conditional) variable is introduced. The method reveals a structure within a family of patterns by considering their vicinity in a feature space along with the similarity of the values assumed by a certain conditional variable. The usefulness of the algorithm is exemplified in the problems
Witold Pedrycz
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Fuzzy C-Means on Metric Lattice
Automatic Control and Computer Sciences, 2020This work proposes a new clustering algorithm named FINFCM by converting original data into fuzzy interval number (FIN) firstly, then it proofs F that denotes the collection of FINs is a lattice and introduce a novel metric distance based on the results from lattice theory as well as combining them with Fuzzy c-means clustering.
Xiangyan Meng +5 more
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Journal of Intelligent & Fuzzy Systems, 2013
Many variants of fuzzy c-means (FCM) clustering method are applied to crisp numbers but only a few of them are extended to non-crisp numbers, mainly due to the fact that the latter needs complicated equations and exhausting calculations. Vector form of fuzzy c-means (VFCM), proposed in this paper, simplifies the FCM clustering method applying to non ...
Hadi Mahdipour +2 more
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Many variants of fuzzy c-means (FCM) clustering method are applied to crisp numbers but only a few of them are extended to non-crisp numbers, mainly due to the fact that the latter needs complicated equations and exhausting calculations. Vector form of fuzzy c-means (VFCM), proposed in this paper, simplifies the FCM clustering method applying to non ...
Hadi Mahdipour +2 more
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Fuzzy C-means and fuzzy swarm for fuzzy clustering problem
Expert Systems with Applications, 2011Fuzzy clustering is an important problem which is the subject of active research in several real-world applications. Fuzzy c-means (FCM) algorithm is one of the most popular fuzzy clustering techniques because it is efficient, straightforward, and easy to implement.
Hesam Izakian, Ajith Abraham
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On the use of the weighted fuzzy c-means in fuzzy modeling
Advances in Engineering Software, 2005zbMATH Open Web Interface contents unavailable due to conflicting licenses.
George E Tsekouras
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A multivariate fuzzy c-means method
Applied Soft Computing, 2013Fuzzy c-means (FCMs) is an important and popular unsupervised partitioning algorithm used in several application domains such as pattern recognition, machine learning and data mining. Although the FCM has shown good performance in detecting clusters, the membership values for each individual computed to each of the clusters cannot indicate how well the
Bruno A. Pimentel +1 more
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Categorical fuzzy entropy c-means
2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2020Hard and fuzzy clustering algorithms are part of the partition-based clustering family. They are widely used in real-world applications to cluster numerical and categorical data. While in hard clustering an object is assigned to a cluster with certainty, in fuzzy clustering an object can be assigned to different clusters given a membership degree.
Abdoul Jalil Djiberou Mahamadou +3 more
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Fuzzy c-means in an MDL-framework
Proceedings 15th International Conference on Pattern Recognition. ICPR-2000, 2002In this paper we present a minimum description length (MDL) framework for fuzzy clustering algorithms. This framework enables us to find an optimal number of cluster centers. We applied our approach to the fuzzy c-means algorithm for which we designed a computationally efficient procedure.
Alexander Selb +2 more
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Generalizations of Fuzzy c-Means and Fuzzy Classifiers
2016Different methods of generalized fuzzy c-means having cluster size variables and cluster covariance variables are compared, which include Gustafson-Kessel’s method, Ichihashi’s method of KL-information, and Yang’s method of fuzzified maximum likelihood.
Sadaaki Miyamoto +2 more
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