Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Uncertain Fuzzy Clustering: Insights and Recommendations
IEEE Computational Intelligence Magazine
The possibilistic C-means algorithm: insights and recommendations
IEEE Transactions on Fuzzy Systems
Comments on “A possibilistic approach to clustering”
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Improved possibilistic C-means clustering algorithms
IEEE Transactions on Fuzzy Systems
Uncertain Fuzzy Clustering: Interval Type-2 Fuzzy Approach to C-Means
IEEE Transactions on Fuzzy Systems
Applying I-Fuzzy Partitions to Represent Sets of Fuzzy Partitions
Proceedings of the 2010 conference on Artificial Intelligence Research and Development: Proceedings of the 13th International Conference of the Catalan Association for Artificial Intelligence
I-fuzzy partitions for representing clustering uncertainties
ICAISC'10 Proceedings of the 10th international conference on Artificial intelligence and soft computing: Part I
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The Possibilistic C-means (PCM) was proposed to overcome some of the drawbacks associated with the Fuzzy C-means (FCM) such as improved performance for noise data. However, PCM possesses some drawbacks such as sensitivity in the initial parameter values and to patterns that have relatively short distances between the prototypes. To overcome theses drawbacks, we propose an interval type-2 fuzzy approach to PCM by considering uncertainty in the fuzzy parameter m in the PCM algorithm.