Negative sequence analysis: A review

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Journal Article
ACM Computing Surveys, 2019, 52, (2)
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© 2019 Association for Computing Machinery. 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.
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