Dichotomic Pattern Mining with Applications to Intent Prediction from Semi-Structured Clickstream Datasets

2022/01/23 by Xin Wang, Wang, Xin, Serdar Kadıoğlu +1
Computer Science · #Advanced Text Analysis Techniques #Artificial Intelligence (cs.AI) #Data Mining Algorithms and Applications #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Machine Learning (cs.LG) #Rough Sets and Fuzzy Logic

paper · pdf · doi:10.48550/arxiv.2201.09178

openalex publication_date 2022/01/23 · openalex created_date 2022/04/03 · openalex updated_date 2026/07/28

Abstract

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. Finally, we present an application on customer intent prediction from digital clickstream data. Overall, we show that pattern embeddings play an integrator role between semi-structured data and machine learning models, improve the performance of the downstream task and retain interpretability.

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