Interactive personalized interesting pattern discovery

The traditional approach of mining frequent patterns generates a very large number of patterns of which a substantial fraction are not much interesting for many data analysis tasks. So selecting a small number of patterns from the large output set such that the selected patterns best align with a particular user's interest is an important task. Existing works on pattern summarization do not help, as these approaches solve interesting pattern discovery from a global perspective which is far from personalization what is needed to meet the pattern discovery demand of a specific user. In this work, we propose an interactive pattern discovery framework, 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. We develop a gradient boosted regression tree based iterative learning algorithm that uses a limited number of interactive feedback from the user to learn her interestingness profile of the patterns, and use this profile for pattern recommendation. 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 the performance of the proposed method is substantially superior to the existing methods.