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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]