  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.
