Algorithms for clustering data
Algorithms for clustering data
Unsupervised Optimal Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural networks in designing fuzzy systems for real world applications
Fuzzy Sets and Systems
A new cluster validity index for the fuzzy c-mean
Pattern Recognition Letters
Toward Improved Ranking Metrics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Fuzzy Possibility C-Mean Based on Complete Mahalanobis Distance and Separable Criterion
ISDA '08 Proceedings of the 2008 Eighth International Conference on Intelligent Systems Design and Applications - Volume 01
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The well known fuzzy partition clustering algorithms are most based on Euclidean distance function, which can only be used to detect spherical structural clusters. Many improved algorithms have been developed to detect non-spherical structural clusters. In our previous work, vectors were represented as objects of the feature space, we found that the difference of vectors can reflect the shape similarity message of these different objects, and we proposed the morphology similarity distance (MSD) for similarity estimation. In this paper, an improved fuzzy partition clustering algorithm, "fuzzy c-mean based on the morphology similarity distance (FCMMSD)", is proposed. This new algorithm has been tested on the Iris data set from the UCI repository. Experiment results prove that the performance of the FCM-MSD algorithm is better than those of the FCM algorithm based on the traditional Euclidean and Manhattan distances.