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VM-NSP: Vertical negative sequential pattern mining with Loose negative Element Constraints

ACM TRANSACTIONS ON INFORMATION SYSTEMS 2021年第2期39卷 p1-27页

作者： Wang, Wei Cao, Longbing Univ Technol Sydney Data Sci Lab Sydney NSW Australia

negative sequential patterns (NSPs) capture more informative and actionable knowledge than classic positive sequential patterns (PSPs) due to the involvement of both occurring and nonoccurring behaviors and events, which can contribute to many relevant applications. However, NSP mining is nontrivial, as it involves fundamental challenges requiring distinct theoretical foundations and is not directly addressable by PSP mining. In the very limited research reported on NSP mining, a negative element constraint (NEC) is incorporated to only consider the NSPs composed of specific forms of elements (containing either positive or negative items), which results in many valuable NSPs being missed. Here, we loosen the NEC (called loose negative element constraint (LNEC)) to include partial negative elements containing both positive and negative items, which enables the discovery of more flexible patterns but incorporates significant new learning challenges, such as representing and mining complete NSPs. Accordingly, we formalize the LNEC-based NSP mining problem and propose a novel vertical NSP mining framework, VM-NSP, to efficiently mine the complete set of NSPs by a vertical representation (VR) of each sequence. An efficient bitmap-based vertical NSP mining algorithm, bM-NSP, introduces a bitmap hash table-based VR and a prefix-based negative sequential candidate generation strategy to optimize the discovery performance. VM-NSP and its implementation bM-NSP form the first VR-based approach for complete NSP mining with LNEC. Theoretical analyses and experiments confirm the performance superiority of bM-NSP on synthetic and real-life datasets w.r.t. diverse data factors, which substantially expands existing NSP mining methods toward flexible NSP discovery.

关键词： Sequence analysis negative sequence analysis negative sequential pattern mining vertical representation nonoccurring behavior analytics behavior informatics

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negative Sequence Analysis: A Review

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ACM COMPUTING SURVEYS 2019年第2期52卷 32-32页

作者： Wang, Wei Cao, Longbing Univ Technol Sydney UTS Adv Analyt Inst Fac Engn & IT POB 123 Broadway Sydney NSW 2007 Australia

negative sequential patterns (NSPs) produced by negative sequence analysis (NSA) capture more informative and actionable knowledge than classic positive sequential patterns (PSPs) due to involving both occurring and nonoccurring items, which appear in many applications. However, the research on NSA is still at an early stage, and NSP mining involves very high computational complexity and a very large search space, there is no widely accepted problem statement on NSP mining, and different settings on constraints and negative containment have been proposed in existing work. Among existing NSP mining algorithms, there are no general and systemic evaluation criteria available to assess them comprehensively. This article conducts a comprehensive technical review of existing NSA research. We explore and formalize a generic problem statement of NSA;investigate, compare, and consolidate the definitions of constraints and negative containment;and compare the working mechanisms and efficiency of existing NSP mining algorithms. The review is concluded by discussing new research opportunities in NSA.

关键词： negative sequential pattern mining negative sequence analysis nonoccurring behavior analysis behavior analytics

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