Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
L1-norm based fuzzy clustering
Fuzzy Sets and Systems
Characterization and detection of noise in clustering
Pattern Recognition Letters
The nature of statistical learning theory
The nature of statistical learning theory
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Robust clustering methods: a unified view
IEEE Transactions on Fuzzy Systems
Fuzzy order statistics and their application to fuzzy clustering
IEEE Transactions on Fuzzy Systems
Generalized fuzzy c-means clustering strategies using Lp norm distances
IEEE Transactions on Fuzzy Systems
Robust fuzzy clustering neural network based on ε-insensitive loss function
Applied Soft Computing
Performing clustering analysis on collaborative models
Intelligent Data Analysis
Robust fuzzy relational classifier incorporating the soft class labels
Pattern Recognition Letters
Graph nodes clustering with the sigmoid commute-time kernel: A comparative study
Data & Knowledge Engineering
Clustering: A neural network approach
Neural Networks
On support vector regression machines with linguistic interpretation of the kernel matrix
Fuzzy Sets and Systems
A time-domain-constrained fuzzy clustering method and its application to signal analysis
Fuzzy Sets and Systems
Enhanced neural gas network for prototype-based clustering
Pattern Recognition
A robust fuzzy local information C-means clustering algorithm
IEEE Transactions on Image Processing
Robust relief-feature weighting, margin maximization, and fuzzy optimization
IEEE Transactions on Fuzzy Systems
Analysis of parameter selections for fuzzy c-means
Pattern Recognition
Robust kernel fuzzy clustering
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part I
An exploration of improving collaborative recommender systems via user-item subgroups
Proceedings of the 21st international conference on World Wide Web
A novel kernelized fuzzy C-means algorithm with application in medical image segmentation
Artificial Intelligence in Medicine
Weighted fuzzy clustering for capability-driven service aggregation
Service Oriented Computing and Applications
Fuzzy partition based soft subspace clustering and its applications in high dimensional data
Information Sciences: an International Journal
Robust constrained fuzzy clustering
Information Sciences: an International Journal
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Fuzzy clustering helps to find natural vague boundaries in data. The Fuzzy C-Means method (FCM) is one of the most popular clustering methods based on minimization of a criterion function. However, one of the greatest disadvantages of this method is its sensitivity to presence of noise and outliers in data. This paper introduces a new ε-insensitive Fuzzy C-Means (εFCM) clustering algorithm. As a special case, this algorithm includes the well-known Fuzzy C-Medians method (FCMED). Also, methods with insensitivity control named αFCM and βFCM are introduced. Performance of the new clustering algorithm is experimentally compared with the FCM method using synthetic data with outliers and heavy-tailed and overlapped groups of data in background noise.