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Projected fuzzy C-means with probabilistic neighbors
Information Sciences, 2022zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Wang, Jikui +5 more
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Unconstrained Fuzzy C-Means Algorithm
IEEE Transactions on Pattern Analysis and Machine IntelligenceFuzzy C-Means algorithm (FCM) is one of the most commonly used fuzzy clustering algorithm, which uses the alternating optimization algorithm to update the membership matrix and the cluster center matrix. FCM achieves effective results in clustering tasks.
Feiping Nie +3 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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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
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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
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Effective Fuzzy Possibilistic C-Means
Proceedings of the ASE BigData & SocialInformatics 2015, 2015Using clustering analysis for identifying cancer types in Colon cancer dataset is extremely difficult task because of high-dimensionality gene with noise. Most of the existing clustering methods for colon to achieve types of cancers often hamper the interpretability of the structure.
S. Ramathilagam, S. R. Kannan, R. Devi
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2019 5th Conference on Knowledge Based Engineering and Innovation (KBEI), 2019
Fuzzy c-means (FCM) is one of the most popular fuzzy clustering methods and it is used in various applications in computer science. Most clustering methods including FCM, suffer from bad initialization problem. If initial cluster centers (membership degree initialization in FCM) are not selected appropriately, it may yield poor results.
Yoosof Mashayekhi +3 more
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Fuzzy c-means (FCM) is one of the most popular fuzzy clustering methods and it is used in various applications in computer science. Most clustering methods including FCM, suffer from bad initialization problem. If initial cluster centers (membership degree initialization in FCM) are not selected appropriately, it may yield poor results.
Yoosof Mashayekhi +3 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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Fuzzy C-Means and Fuzzy TLBO for Fuzzy Clustering
2015The choice of initial center plays a great role in achieving optimal clustering results in all partitional clustering approaches. Fuzzy C-means is a widely used approach but it also gets trapped in local optima values due to sensitiveness to initial cluster centers.
P. Gopala Krishna, D. Lalitha Bhaskari
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Fuzzy C-Means in High Dimensional Spaces
International Journal of Fuzzy System Applications, 2011High dimensions have a devastating effect on the FCM algorithm and similar algorithms. One effect is that the prototypes run into the centre of gravity of the entire data set. The objective function must have a local minimum in the centre of gravity that causes FCM’s behaviour. In this paper, examine this problem.
Winkler, Roland +2 more
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Fuzzy c-means clustering of incomplete data
IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 2001The problem of clustering a real s-dimensional data set X={x(1 ),,,,,x(n)} subset R(s) is considered. Usually, each observation (or datum) consists of numerical values for all s features (such as height, length, etc.), but sometimes data sets can contain vectors that are missing one or more of the feature values.
R J, Hathaway, J C, Bezdek
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