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In data sets some attributes may have higher or lower importance. One of the tools used for data analysis of such datasets are subspace neuro-fuzzy systems. They elaborate fuzzy rules to describe data sets. In subspace neuro-fuzzy systems fuzzy rules exist in subspaces defined with subsets of attributes. In the paper we propose a novel fuzzy biclustering algorithm that groups both objects and attributes in fuzzy clusters. In that way we create a subspace fuzzy rule base for a subspace fuzzy system. The paper is accompanied with numerical examples that show this approach can lead to better generalisation (and thus lower data prediction errors) with preserved interpretation of fuzzy models.