A global Constraint for mining Sequential Patterns with GAP constraint
원문 초록
Sequential pattern mining (SPM) under gap constraint is a challenging task. Many efficient specialized methods have been developed but they are all suffering from a lack of genericity. The Constraint Programming (CP) approaches are not so effective because of the size of their encodings. In[7], we have proposed the global constraint Prefix-Projection for SPM which remedies to this drawback. However, this global constraint cannot be directly extended to support gap constraint. In this paper, we propose the global constraint GAP-SEQ enabling to handle SPM with or without gap constraint. GAP-SEQ relies on the principle of right pattern extensions. Experiments show that our approach clearly outperforms both CP approaches and the state-of-the-art cSpade method on large datasets.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Causal Analysis of Customer Churn Using Deep Learning
Mining compact high utility sequential patterns
HUSP-SP: Faster Utility Mining on Sequence Data
Leveraging Language Foundation Models for Human Mobility Forecasting
Memory-Efficient Sequential Pattern Mining with Hybrid Tries
Supervised sequential pattern mining of event sequences in sport to identify important patterns of play: an application to rugby union
토론
이 논문과 연결된 포럼 글이 아직 없습니다.