DeepAI AI Chat
Log In Sign Up

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

by   Xin Wang, et al.

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.


page 1

page 2

page 3

page 4


A Subsequence Interleaving Model for Sequential Pattern Mining

Recent sequential pattern mining methods have used the minimum descripti...

A Semi-supervised Multi-task Learning Approach to Classify Customer Contact Intents

In the area of customer support, understanding customers' intents is a c...

TaSPM: Targeted Sequential Pattern Mining

Sequential pattern mining (SPM) is an important technique of pattern min...

Constraint-based Sequential Pattern Mining with Decision Diagrams

Constrained sequential pattern mining aims at identifying frequent patte...

Pattern-Based Classification: A Unifying Perspective

The use of patterns in predictive models is a topic that has received a ...

Scaling pattern mining through non-overlapping variable partitioning

Biclustering algorithms play a central role in the biotechnological and ...