Dichotomic Pattern Mining Integrated With Constraint Reasoning for Digital Behavior Analysis

Sequential pattern mining remains a challenging task due to the large number of redundant candidate patterns and the exponential search space. In addition, further analysis is still required to map extracted patterns to different outcomes. In this paper, we introduce a pattern mining framework that...

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Bibliographic Details
Published in:Frontiers in artificial intelligence 2022-07, Vol.5, p.868085-868085
Main Authors: Ghosh, Sohom, Yadav, Shefali, Wang, Xin, Chakrabarty, Bibhash, Kadıoğlu, Serdar
Format: Article
Language:English
Subjects:
Artificial Intelligence
dichotomic pattern mining
digital behavior analysis
intent prediction
intrusion detection
semi-structured clickstream datasets
sequential pattern mining
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Summary:Sequential pattern mining remains a challenging task due to the large number of redundant candidate patterns and the exponential search space. In addition, further analysis is still required to map extracted patterns to different outcomes. In this paper, we introduce a pattern mining framework that operates on semi-structured datasets and exploits the dichotomy between outcomes. Our approach takes advantage of constraint reasoning to find sequential patterns that occur frequently and exhibit desired properties. This allows the creation of novel pattern embeddings that are useful for knowledge extraction and predictive modeling. Based on dichotomic pattern mining, we present two real-world applications for customer intent prediction and intrusion detection. Overall, our approach plays an integrator role between semi-structured sequential data and machine learning models, improves the performance of the downstream task, and retains interpretability.
ISSN:2624-8212
2624-8212
DOI:10.3389/frai.2022.868085