Abstract：Frequent pattern mining(FPM) is one of the key tasks of graph data mining. The objective of FPM is to extract patterns with support values higher than predefined thresholds from large-scale graph data. However, constrained by single-dimensional evaluation metrics and neglect of subjective preferences, traditional FPM methods often fail to align mining results with the expectations of users. To address this issue, a certified pseudo-label enhanced active learning framework for pattern interest evaluation(CPALF) is proposed. CPALF is designed to accurately predict subjective pattern preferences of users through minimal human interaction. An active learning strategy is employed to efficiently collect the preferences of users via human-computer interaction. CPALF incorporates semi-supervised learning to generate high-confidence pseudo-labeled training samples from unlabeled data, thereby significantly improving prediction performance while reducing annotation dependency. Experiments demonstrate that CPALF effectively captures the preferences of users with high prediction accuracy under limited labeled data.
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