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.

在数据集中，某些属性可能具有更高或更低的重要性。用于此类数据集的数据分析的工具之一是子空间神经模糊系统。他们制定了描述数据集的模糊规则。在子空间神经模糊系统中，模糊规则存在于用属性子集定义的子空间中。在本文中，我们提出了一种新颖的模糊双聚类算法，将对象和属性分组到模糊聚类中。通过这种方式，我们为子空间模糊系统创建了子空间模糊规则库。该论文附有数值示例，表明这种方法可以更好地泛化（从而降低数据预测误差），同时保留对模糊模型的解释。