Unsupervised attribute reduction algorithm framework based on spectral clustering and attribute significance function

Attribute reduction is a significant challenge in fields like data mining and pattern recognition. Various models have been introduced to enhance the performance of attribute reduction algorithms, such as the fuzzy rough sets model. However, the common greedy-based reduction algorithm frameworks shared by these models often struggle to efficiently remove redundant attributes. Manual intervention is often employed by researchers to extract the optimal attribute subset, such as setting hyperparameters to control the algorithm's progression. Unfortunately, these methods lack practical relevance. To address these challenges, this study presents an unsupervised attribute reduction algorithm framework that employs spectral clustering and an attribute significance function. Initially, we introduce an attribute similarity function and a spectral clustering algorithm to capture the data's main partition structures. We then propose a method for automatically selecting the optimal clustering result, aiming to generate preliminary reduction outcomes. Additionally, we developed a novel unsupervised attribute reduction framework by integrating it with the traditional approach. Furthermore, a specific unsupervised attribute reduction algorithm has been obtained by embedding an unsupervised attribute significance function. Comparative experiments were conducted with six state-of-the-art algorithms across 27 datasets, and the results show that our proposed algorithm demonstrates superior efficiency and effectiveness in attribute selection.