Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised fuzzy clustering with multi-center clusters
Fuzzy Sets and Systems - Clustering and modeling
A probabilistic framework for semi-supervised clustering
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Enhancing semi-supervised clustering: a feature projection perspective
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Robust self-tuning semi-supervised learning
Neurocomputing
IEEE Transactions on Fuzzy Systems
Modified fuzzy c-means for ordinal valued attributes with particle swarm for optimization
Fuzzy Sets and Systems
Expert Systems with Applications: An International Journal
Spectral clustering with fuzzy similarity measure
Digital Signal Processing
A novel multiseed nonhierarchical data clustering technique
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fuzzy spectral clustering with robust spatial information for image segmentation
Applied Soft Computing
Fuzzy c-means improvement using relaxed constraints support vector machines
Applied Soft Computing
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Fuzzy C-means (FCM) clustering has been widely used successfully in many real-world applications. However, the FCM algorithm is sensitive to the initial prototypes, and it cannot handle non-traditional curved clusters. In this paper, a multi-center fuzzy C-means algorithm based on transitive closure and spectral clustering (MFCM-TCSC) is provided. In this algorithm, the initial guesses of the locations of the cluster centers or the membership values are not necessary. Multi-centers are adopted to represent the non-spherical shape of clusters. Thus, the clustering algorithm with multi-center clusters can handle non-traditional curved clusters. The novel algorithm contains three phases. First, the dataset is partitioned into some subclusters by FCM algorithm with multi-centers. Then, the subclusters are merged by spectral clustering. Finally, based on these two clustering results, the final results are obtained. When merging subclusters, we adopt the lattice similarity method as the distance between two subclusters, which has explicit form when we use the fuzzy membership values of subclusters as the features. Experimental results on two artificial datasets, UCI dataset and real image segmentation show that the proposed method outperforms traditional FCM algorithm and spectral clustering obviously in efficiency and robustness.