In this paper, we study the problem of discovering interesting patterns through user's interactive feedback. We assume a set of candidate patterns (ie, frequent patterns) has already been mined. Our goal is to help a particular user effectively discover interesting patterns according to his specific interest. Without requiring a user to explicitly construct a prior knowledge to measure the interestingness of patterns, we learn the user's prior knowledge from his interactive feedback. We propose two models to represent a user's prior: the  log linear model  and  biased belief model . The former is designed for item-set patterns, whereas the latter is also applicable to sequential and structural patterns. To learn these models, we present a two-stage approach,  progressive shrinking  and  clustering , to select sample patterns for feedback. The experimental results on real and synthetic data sets demonstrate the effectiveness of our approach.
['Dong Xin', 'Xuehua Shen', 'Qiaozhu Mei', 'Jiawei Han']
Discovering interesting patterns through user's interactive feedback