Characterization and detection of noise in clustering
Pattern Recognition Letters
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
An enhanced possibilistic C-Means clustering algorithm EPCM
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Comments on “A possibilistic approach to clustering”
IEEE Transactions on Fuzzy Systems
A Possibilistic Fuzzy c-Means Clustering Algorithm
IEEE Transactions on Fuzzy Systems
Efficient inhomogeneity compensation using fuzzy c-means clustering models
Computer Methods and Programs in Biomedicine
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One of the main challenges in the field of c-means clustering models is creating an algorithm that is both accurate and robust. In the absence of outlier data, the conventional probabilistic fuzzy c-means (FCM) algorithm, or the latest possibilistic-fuzzy mixture model (PFCM), provide highly accurate partitions. However, during the 30- year history of FCM, the researcher community of the field failed to produce an algorithm that is accurate and insensitive to outliers at the same time. This paper introduces a novel mixture clustering model built upon probabilistic and possibilistic fuzzy partitions, where the two components are connected to each other in a qualitatively different way than they were in earlier mixtures. The fuzzy-possibilistic product partition c-means (FP3CM) clustering algorithm seems to fulfil the initial requirements, namely it successfully suppresses the effect of outliers situated at any finite distance and provides partitions of high quality.