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In this paper, we propose a new semi-supervised fuzzy co-clustering algorithm called SS-FCC for categorization of large web documents. In this new approach, the clustering process is carried out by incorporating some prior domain knowledge of a dataset in the form of pairwise constraints provided by users into the fuzzy co-clustering framework. With the help of those constraints, the clustering problem is formulated as the problem of maximizing a competitive agglomeration cost function with fuzzy terms, taking into account the provided domain knowledge. The constraint specifies whether a pair of objects 'must' or 'cannot' be clustered together. The update rules for fuzzy memberships are derived, and an iterative algorithm is designed for the soft co-clustering process. Our experimental studies show that the quality of clustering results can be improved significantly with the proposed approach. Simulations on 10 large benchmark datasets demonstrate the strength and potentials of SS-FCC in terms of performance evaluation criteria, stability and operating time, compared with some of the existing semi-supervised algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we develop a new soft model dual fuzzy-possibilistic coclustering (DFPC) for document categorization. The proposed model targets robustness to outliers and richer representations of coclusters. DFPC is inspired by an existing algorithm called possibilistic fuzzy C-means (PFCM) that hybridizes fuzzy and possibilistic clustering. It has been shown that PFCM can perform effectively for low-dimensional data clustering. To achieve our goal, we expand this existing idea by introducing a novel PFCM-like coclustering model. The new algorithm DFPC preserves the desired properties of PFCM. In addition, as a coclustering algorithm, DFPC is more suitable for our intended high-dimensional application: document clustering. Besides, the coclustering mechanism enables DFPC to generate, together with document clusters, fuzzy-possibilistic word memberships. These word memberships, which are absent in the existing PFCM model, can play an important role in generating useful descriptions of document clusters. We detail the formulation of the proposed model and provide an extensive analytical study of the algorithm DFPC. Experiments on an artificial dataset and various benchmark document datasets demonstrate the effectiveness and potential of DFPC. [ABSTRACT FROM AUTHOR]

Clustering is often seen as a more practical but very challenging answer to the task of categorizing objects. Minimum Sum-squared Residue for Fuzzy Co-Clustering (MSR-FCC) is proposed to address two issues faced by many existing clustering algorithms, namely the high-dimensionality and the inherent fuzziness found in most real-world data. MSR-FCC is able to simultaneously cluster data and features using fuzzy techniques. It suggests a new partitioning fuzzy co-clustering algorithm based on the mean squared residue approach. Besides handling overlap clusters, MSR-FCC offers the flexibility that allows the number of data clusters to be different from the number of feature clusters, which reflects the distribution characteristic inherited in real-world data. In this paper, mathematical formulation of MSR-FCC is derived and explained. Experiments were conducted on standard datasets to demonstrate that the proposed algorithm is able to cluster high-dimensional data with overlaps feasibly and at the same time, it provides a new and promising mechanism for improving the interpretability of the co-clusters through the fuzzy membership function. [ABSTRACT FROM AUTHOR]