Acquisition-Oriented Incomplete Multi-View Clustering under Limited Budgets

Incomplete multi-view clustering (IMVC) typically recovers missing view information from the observed data, overlooking practical scenarios where missing sample-view entries can instead be directly acquired at a cost. This raises a fundamental question: how should a limited acquisition budget be allocated to improve multi-view clustering? To address this problem, we propose Evidence-Adaptive Acquisition (EAA), a fully unsupervised framework for acquisition-aware incomplete multi-view clustering. Specifically, EAA first assesses whether the available observations in each view are sufficient to support reliable entry-level probabilistic modeling. With sufficient observations, EAA quantifies the acquisition value of each missing entry through its conditional information gain about the latent cluster assignment and selectively acquires the most informative entries. Under insufficient observations, EAA avoids unreliable fine-grained utility estimation and instead selects views by residuals and samples eligible missing entries uniformly within those views. The acquisition policy iteratively incorporates newly acquired entries to update the model and reassess the remaining candidates, enabling adaptive budget allocation under a strict acquisition budget. Extensive experiments on multiple datasets demonstrate that EAA effectively allocates limited acquisition budgets and improves clustering performance over existing acquisition and incomplete multi-view clustering methods.