Unsupervised attribute reduction based on neighborhood dependency

Neighborhood rough set theory is an important computational model in granular computing and has been successfully applied in many areas. One of its most prominent applications is in attribute reduction. However, most current attribute reduction methods for neighborhood rough sets are supervised or semi-supervised, which makes them unable to handle datasets without decision information. To address this, we propose an unsupervised attribute reduction strategy based on neighborhood dependency. First, a neighborhood rough set model based on conditional attribute sets is constructed. Then, based on all individual attribute subsets in the datasets, the importance of the attributes is defined to indicate the significance of the candidate attributes. Furthermore, a neighborhood dependency-based unsupervised attribute reduction (NDUAR) algorithm is designed. Finally, NDUAR is compared with existing algorithms on publicly available datasets. The experimental results show that NDUAR can select fewer attributes to maintain or improve the performance of the clustering algorithm. The effectiveness of the algorithm proposed in this paper is thereby confirmed.