Abstract
Fuzzy clustering has received significant attention due to its robust capability for handling uncertain data and its strong clustering performance. Recently, multi-view fuzzy clustering methods have become a popular research focus for multi-view data. However, existing methods often suffer from low computational efficiency because of the extensive distance calculations required during iterative optimization. Moreover, the challenge of adaptively and comprehensively extracting discriminative information across views to enhance clustering performance remains largely unresolved. To address these issues, this paper proposes a new adaptive multi-view fuzzy clustering method based on diverse anchors guidance (AMVFC_DAG). Based on anchor graph theory, the proposed method first introduces a new diverse anchor graph learning method with dual information exploration to capture both multi-scale common and specific information across views. To further improve clustering efficiency, the proposed method integrates this diverse anchor graph learning into a unified multi-view clustering framework with multi-scale anchors, enabling mutual enhancement between components and thereby boosting overall performance. Furthermore, adaptive scale and view weighting are incorporated to enable flexible clustering. Finally, we propose an adaptive fusion mechanism that combines the learned anchor graphs with an anchor membership matrix, to produce the final cluster assignments. Extensive experiments on multiple multi-view datasets demonstrate the proposed method's effectiveness in both clustering performance and computational efficiency.