PRIIME: A Generic Framework for Interactive Personalized Interesting Pattern Discovery

TL;DR

PRIIME is a flexible, interactive framework that personalizes the discovery of interesting patterns in data, supporting sequences and graphs, by learning user preferences through limited feedback and outperforming existing methods.

Contribution

It introduces a novel, user-interactive pattern discovery framework that does not require prior interest measures and supports multiple pattern types using neural networks.

Findings

Outperforms existing interactive pattern discovery methods

Effective in real-world datasets including a real-estate case study

Successfully learns user preferences with limited feedback

Abstract

The traditional frequent pattern mining algorithms generate an exponentially large number of patterns of which a substantial proportion are not much significant for many data analysis endeavors. Discovery of a small number of personalized interesting patterns from the large output set according to a particular user's interest is an important as well as challenging task. Existing works on pattern summarization do not solve this problem from the personalization viewpoint. In this work, we propose an interactive pattern discovery framework named PRIIME which identifies a set of interesting patterns for a specific user without requiring any prior input on the interestingness measure of patterns from the user. The proposed framework is generic to support discovery of the interesting set, sequence and graph type patterns. We develop a softmax classification based iterative learning algorithm that uses a limited number of interactive feedback from the user to learn her interestingness profile, and use this profile for pattern recommendation. To handle sequence and graph type patterns PRIIME adopts a neural net (NN) based unsupervised feature construction approach. We also develop a strategy that combines exploration and exploitation to select patterns for feedback. We show experimental results on several real-life datasets to validate the performance of the proposed method. We also compare with the existing methods of interactive pattern discovery to show that our method is substantially superior in performance. To portray the applicability of the framework, we present a case study from the real-estate domain.