AW-GBGAE: an Adaptive Weighted Graph Autoencoder Based on Granular-Balls for General Data Clustering

In the current scenario, a vast amount of unlabeled high-dimensional data exhibits intrinsic relationships, making it suitable for information extraction through graph-based clustering methods. However, these datasets often lack edge structure information and contain numerous irrelevant features. To address these challenges, we propose a comprehensive solution that involves: (1) applying a feature weighting approach to manage features, (2) constructing edges based on weighted granular-balls, and (3) integrating graph convolutional networks (GCNs) with edge generation to develop an autoencoder network. Our method significantly enhances the extraction of relevant information from high-dimensional, unlabeled data, improving the overall performance and reliability of the clustering process. Extensive experimental results demonstrate that our model, AW-GBGAE, excels in clustering tasks and exhibits strong competitiveness compared to baseline models. The code is publicly available at https://github.com/xjnine/AWGBGAE.