Justified Cross-View Interaction: Beyond Trivial Alignment in Multi-View Clustering

Deep multi view clustering seeks to discover coherent semantic structures from heterogeneous views by exploiting cross view information. However, existing methods often treat cross view interaction as inherently beneficial, overlooking that information exchanged across views can be unreliable, structurally incompatible, or redundant. To address these issues, we formulate multi view clustering from the perspective of Justified Cross View Interaction (JCVI), where an effective interaction should be both useful and non trivial. Specifically, we develop a utility aware interaction mechanism that jointly characterizes semantic reliability, structural compatibility, and feature complementarity, and adaptively regulates cross view information exchange to selectively incorporate complementary cues while preserving view specific discriminative semantics. To promote interaction non triviality, we further construct a target exclusive supervisory context for each view using only the remaining views, thereby removing the direct self information path and encouraging the target representation to capture semantic structures predictable from genuinely external views. Multi granularity structural constraints are further imposed at pairwise and neighborhood levels to preserve coherent cross view organization. Extensive experiments on five benchmark datasets demonstrate the effectiveness of the proposed framework and its strong clustering performance against representative state of the art methods.