L1-norm based fuzzy clustering
Fuzzy Sets and Systems
ACM Computing Surveys (CSUR)
Fuzzy clustering with squared Minkowski distances
Fuzzy Sets and Systems - Special issue on clustering and learning
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Stability conditions of fuzzy systems and its application to structural and mechanical systems
Advances in Engineering Software
Pattern Recognition Letters
Data Clustering: Theory, Algorithms, and Applications (ASA-SIAM Series on Statistics and Applied Probability)
Kernelized fuzzy attribute C-means clustering algorithm
Fuzzy Sets and Systems
The stability of an oceanic structure with T-S fuzzy models
Mathematics and Computers in Simulation
Fuzzy C-means and fuzzy swarm for fuzzy clustering problem
Expert Systems with Applications: An International Journal
Fuzzy time series forecasting method based on Gustafson-Kessel fuzzy clustering
Expert Systems with Applications: An International Journal
Fuzzy relational clustering around medoids: A unified view
Fuzzy Sets and Systems
Analysis of parameter selections for fuzzy c-means
Pattern Recognition
Analysis of the weighting exponent in the FCM
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Generalized fuzzy c-means clustering strategies using Lp norm distances
IEEE Transactions on Fuzzy Systems
A Possibilistic Fuzzy c-Means Clustering Algorithm
IEEE Transactions on Fuzzy Systems
Mathematical and Computer Modelling: An International Journal
Survey of clustering algorithms
IEEE Transactions on Neural Networks
A weighted multivariate Fuzzy C-Means method in interval-valued scientific production data
Expert Systems with Applications: An International Journal
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Fuzzy 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 individuals are classified. In this paper, a new approach to handle the memberships based on the inherent information in each feature is presented. The algorithm produces a membership matrix for each individual, the membership values are between zero and one and measure the similarity of this individual to the center of each cluster according to each feature. These values can change at each iteration of the algorithm and they are different from one feature to another and from one cluster to another in order to increase the performance of the fuzzy c-means clustering algorithm. To obtain a fuzzy partition by class of the input data set, a way to compute the class membership values is also proposed in this work. Experiments with synthetic and real data sets show that the proposed approach produces good quality of clustering.