TY - GEN
T1 - Discovering interesting patterns through user's interactive feedback
AU - Xin, Dong
AU - Shen, Xuehua
AU - Mei, Qiaozhu
AU - Han, Jiawei
PY - 2006
Y1 - 2006
N2 - 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.
AB - 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.
KW - Interactive Feedback
KW - Pattern Discovery
UR - http://www.scopus.com/inward/record.url?scp=33749566545&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33749566545&partnerID=8YFLogxK
U2 - 10.1145/1150402.1150502
DO - 10.1145/1150402.1150502
M3 - Conference contribution
AN - SCOPUS:33749566545
SN - 1595933395
SN - 9781595933393
T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
SP - 773
EP - 778
BT - KDD 2006
PB - Association for Computing Machinery
T2 - KDD 2006: 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Y2 - 20 August 2006 through 23 August 2006
ER -