In this paper, we introduce a new algorithm called Dual Fuzzy-possibilistic Co-clustering (DFPC) for docu- ment categorization. The proposed algorithm offers several advantages. Firstly, the combined fuzzy and possibilistic cluster memberships in DFPC can provide realistic repre- sentation of document clusters. Secondly, as a co-clustering algorithm, DFPC can categorize high-dimensional datasets effectively. Thirdly, the possibilistic clustering element of the algorithm makes it robust to outliers. We detail the for- mulation of DFPC, and empirically demonstrate its effec- tiveness in categorizing benchmark document datasets.