Discovering interesting patterns through user's interactive feedback

In this paper, we study the problem of discovering interesting patterns through user's interactive feedback. We assume a set of candidate patterns (i.e., 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. Categories and Subject Descriptors:...

• Type: Conference
• Year: 2006
• Where: KDD
• Authors: Dong Xin, Xuehua Shen, Qiaozhu Mei, Jiawei Han
• Keywords: Candidate Patterns, Data Mining, Frequent Patterns, Interesting Patterns, KDD 2006