Abstract

As data becomes a crucial resource for powering various real-world applications, the field of data mining encounters numerous challenges, particularly regarding storage and real-time processing. Mining association rules to uncover relationships and patterns in large datasets is a crucial technique. However, the inherent uncertainty and incompleteness of data pose significant difficulties for traditional mining algorithms. To tackle these challenges, a novel method is proposed for mining sequential rules from uncertain sequence databases (SDs). This method involves two primary steps: first, extracting a set of probabilistic rules, and second, filtering these rules based on the sequential information within the data. This approach effectively addresses data uncertainty and incompleteness, enabling the extraction of meaningful sequential rules that are otherwise difficult to identify using conventional methods. This innovative method enhances the capability of mining algorithms to handle uncertain data, offering a robust solution for real-time data processing as well as storage issues in various applications. Experimental results demonstrate the algorithm's efficiency and scalability on both synthetic and real-world datasets. The proposed method achieved superior runtime and memory efficiency as dataset sizes increase

Keywords

Association rule, Probabilistic database, Sequences database, Sequential rule, Uncertain data.

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