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

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.
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.
Dichotomic Pattern Mining with Applications to Intent Prediction from Semi-Structured Clickstream Datasets
http://arxiv.org/abs/2201.09178v1