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A Novel Fuzzy Entropy-Based Method to Improve the Performance of the Fuzzy C-Means Algorithm

https://doi.org/10.3390/electronics9040554

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Encyclopedia of Machine Learning

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Pattern Recognition with Fuzzy Objective Function Algorithms

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An New Initialization Method for Fuzzy c-Means Algorithm Based on Density

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A new entropy-based approach for fuzzy c-means clustering and its application to brain MR image segmentation

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FCM: The fuzzy c-means clustering algorithm

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Classification assessment methods

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Fuzzy c-means clustering with weighted image patch for image segmentation

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Clustering of solar energy facilities using a hybrid fuzzy c-means algorithm initialized by metaheuristics

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Kernel-based fuzzy c-means clustering algorithm based on genetic algorithm

inferredcites →https://doi.org/10.1016/j.proeng.2011.08.372openalex-snapshot

Fuzzy Entropy Clustering Using Possibilistic Approach

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A definition of a nonprobabilistic entropy in the setting of fuzzy sets theory

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Initializing the Fuzzy C-Means Cluster Center With Particle Swarm Optimization for Sentiment Clustering

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An efficient fuzzy classifier with feature selection based on fuzzy entropy

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The possibilistic C-means algorithm: insights and recommendations

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A Novel Level Set Method with Improved Fuzzy C-Means Based on Genetic Algorithm for Image Segmentation

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An improved pagerank algorithm based on fuzzy C-means clustering and information entropy

inferredcites →https://doi.org/10.1109/icinis.2010.171openalex-snapshot

An Initialization Method for Fuzzy C-means Algorithm Using Subtractive Clustering

inferredcites →https://doi.org/10.1109/icmlc.2003.1259984openalex-snapshot

Study on combining subtractive clustering with fuzzy c-means clustering

inferredcites →https://doi.org/10.1109/smc.2014.6974077openalex-snapshot

Maximum-entropy-based multiple kernel fuzzy c-means clustering algorithm

inferredcites →https://doi.org/10.1109/tfuzz.2008.2009458openalex-snapshot

Density-Weighted Fuzzy c-Means Clustering

inferredcites →https://doi.org/10.1109/tfuzz.2012.2201485openalex-snapshot

Fuzzy c-Means Algorithms for Very Large Data

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Epistasis Analysis Using an Improved Fuzzy C-Means-Based Entropy Approach

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Fuzzy C-Means Clustering With Local Information and Kernel Metric for Image Segmentation

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The Stock Classification Based on Entropy Weight Method and Improved Fuzzy C-means Algorithm

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A Weighted Mean Subtractive Clustering Algorithm

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Software Restructuring at the Function Level Using Entropy Based Fuzzy C-means Clustering for Object Oriented Programming Language

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