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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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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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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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2007 IEEE International Fuzzy Systems Conference, 2007
Recently several algorithms for clustering large data sets or streaming data sets have been proposed. Most of them address the crisp case of clustering, which cannot be easily generalized to the fuzzy case. In this paper, we propose a simple single pass (through the data) fuzzy c means algorithm that neither uses any complicated data structure nor any ...
Prodip Hore +2 more
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Recently several algorithms for clustering large data sets or streaming data sets have been proposed. Most of them address the crisp case of clustering, which cannot be easily generalized to the fuzzy case. In this paper, we propose a simple single pass (through the data) fuzzy c means algorithm that neither uses any complicated data structure nor any ...
Prodip Hore +2 more
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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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Fuzzy c-means for Fuzzy Hierarchical Clustering
The 14th IEEE International Conference on Fuzzy Systems, 2005. FUZZ '05., 2005This paper describes an algorithm for building fuzzy hierarchies. These are hierarchies where the elements can have fuzzy membership to the nodes. The paper presents an approach that mainly follows a bottom-up strategy, and describes the functions needed to operate with fuzzy variables.
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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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Projected fuzzy C-means with probabilistic neighbors
Information Sciences, 2022zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Jikui Wang +5 more
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