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A tree-based framework to mine top-K closed sequential patterns
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AI
DOI：10.1007/s10489-024-06137-y.png)
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Abstract
En 中文
Top-K closed sequential pattern (CSP) mining addresses the challenge of reducing the number of mined patterns and the dependency on the support threshold parameter. This study tackles top-K CSP mining from three angles: top-K generic CSPs, group CSPs, and redundancy-aware CSPs. We propose the novel SP-Tree-based KCloTreeMiner to mine these variations and introduce the PaMHep data structure for efficient candidate pattern maintenance. Two pruning strategies-namely, pattern absorption and SP-Tree-based temporary node projection-are also presented to reduce search space. This study offers a thorough theoretical analysis and establishes bounds for the top-K framework, covering everything from solution design to completeness and optimization. Evaluations on six real-life datasets show up to a 23% average runtime improvement for KCloTreeMiner over the benchmark algorithm TKCS. We also propose two greedy algorithms MaxWC\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$Max_{WC}$$\end{document} and MaxWOC\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$Max_{WOC}$$\end{document} for pattern summarization and introduce Subset Distance for measuring distances between sequential patterns, improving K-medoid clustering results over average silhouette-width for the reported clusters.
Keywords:
Closed sequential patterns
Top-K mining
Tree-based mining
Pruning technique
Clustering
Pattern summarization
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