Characterization and detection of noise in clustering
Pattern Recognition Letters
Gaussian clustering method based on maximum-fuzzy-entropy interpretation
Fuzzy Sets and Systems
Analytical approximations for real values of the Lambert W-function
Mathematics and Computers in Simulation
Spatial models for fuzzy clustering
Computer Vision and Image Understanding
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Knowledge-Based Clustering: From Data to Information Granules
Knowledge-Based Clustering: From Data to Information Granules
Maximum entropy fuzzy clustering with application to real-time target tracking
Signal Processing - Special section: Distributed source coding
Advances in Fuzzy Clustering and its Applications
Advances in Fuzzy Clustering and its Applications
Is there a need for fuzzy logic?
Information Sciences: an International Journal
Computational Statistics & Data Analysis
Data clustering: 50 years beyond K-means
Pattern Recognition Letters
Evaluation of e-learning systems based on fuzzy clustering models and statistical tools
Expert Systems with Applications: An International Journal
Algorithms for Fuzzy Clustering: Methods in c-Means Clustering with Applications
Algorithms for Fuzzy Clustering: Methods in c-Means Clustering with Applications
Expert Systems with Applications: An International Journal
Fuzzy clustering algorithms for unsupervised change detection in remote sensing images
Information Sciences: an International Journal
Fuzzy time series prediction using hierarchical clustering algorithms
Expert Systems with Applications: An International Journal
Fuzzy clustering of time series in the frequency domain
Information Sciences: an International Journal
Colour image segmentation using fuzzy clustering techniques and competitive neural network
Applied Soft Computing
Fuzzy time series forecasting method based on Gustafson-Kessel fuzzy clustering
Expert Systems with Applications: An International Journal
Multivariate fuzzy forecasting based on fuzzy time series and automatic clustering techniques
Expert Systems with Applications: An International Journal
Comparison of clustering methods: A case study of text-independent speaker modeling
Pattern Recognition Letters
A new approach to fuzzification of memberships in cluster analysis
MDAI'05 Proceedings of the Second international conference on Modeling Decisions for Artificial Intelligence
Effective fuzzy c-means clustering algorithms for data clustering problems
Expert Systems with Applications: An International Journal
A hybrid device for the solution of sampling bias problems in the forecasting of firms' bankruptcy
Expert Systems with Applications: An International Journal
Supplier selection using a novel intuitionist fuzzy clustering approach
Applied Soft Computing
IEEE Transactions on Signal Processing
Reducing the time complexity of the fuzzy c-means algorithm
IEEE Transactions on Fuzzy Systems
A Possibilistic Fuzzy c-Means Clustering Algorithm
IEEE Transactions on Fuzzy Systems
A possibilistic approach to clustering
IEEE Transactions on Fuzzy Systems
Switching regression models and fuzzy clustering
IEEE Transactions on Fuzzy Systems
The fuzzy c spherical shells algorithm: A new approach
IEEE Transactions on Neural Networks
Revenue forecasting using a least-squares support vector regression model in a fuzzy environment
Information Sciences: an International Journal
Online extraction of main linear trends for nonlinear time-varying processes
Information Sciences: an International Journal
A fuzzy classifier approach to estimating software quality
Information Sciences: an International Journal
A constraint propagation approach to structural model based image segmentation and recognition
Information Sciences: an International Journal
Segmentation of color images using a linguistic 2-tuples model
Information Sciences: an International Journal
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Pattern recognition is a collection of computer techniques to classify various observations into different clusters of similar attributes in either supervised or unsupervised manner. Application of fuzzy logic to unsupervised classification or clustering methods has resulted in many wildly used techniques such as fuzzy c-means (FCM) method. However, when the observations are too noisy, the performance of such methods might be reduced. Thus, in this paper, a new fuzzy clustering method based on FCM is presented and the relative entropy is added to its objective function as a regularization function to maximize the dissimilarity between clusters. Several examples are provided to examine the performance of the proposed clustering method. The obtained results show that the proposed method has a very good ability in detecting noises and assignment of suitable membership degrees to the observations.