Discovering interesting patterns through user's interactive feedback

Authors:
Dong Xin;Xuehua Shen;Qiaozhu Mei;Jiawei Han
Affiliations:
University of Illinois at Urbana-Champaign, Urbana, IL;University of Illinois at Urbana-Champaign, Urbana, IL;University of Illinois at Urbana-Champaign, Urbana, IL;University of Illinois at Urbana-Champaign, Urbana, IL
Venue:
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Year:
2006

Citing 10
Cited 8

Algorithms for clustering data

Algorithms for clustering data
An introduction to support Vector Machines: and other kernel-based learning methods

An introduction to support Vector Machines: and other kernel-based learning methods
What Makes Patterns Interesting in Knowledge Discovery Systems

IEEE Transactions on Knowledge and Data Engineering
Optimizing search engines using clickthrough data

Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
SNOPT: An SQP Algorithm for Large-Scale Constrained Optimization

SIAM Review
Active feedback in ad hoc information retrieval

Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Fast discovery of unexpected patterns in data, relative to a Bayesian network

Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
SVM selective sampling for ranking with application to data retrieval

Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Mining compressed frequent-pattern sets

VLDB '05 Proceedings of the 31st international conference on Very large data bases
Learning to order things

Journal of Artificial Intelligence Research

Domain ontology driven data mining: a medical case study

Proceedings of the 2007 international workshop on Domain driven data mining
Mining frequent arrangements of temporal intervals

Knowledge and Information Systems
Approximate weighted frequent pattern mining with/without noisy environments

Knowledge-Based Systems
Actionable knowledge discovery and delivery

Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Knowledge discovery interestingness measures based on unexpectedness

Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Interactive pattern mining on hidden data: a sampling-based solution

Proceedings of the 21st ACM international conference on Information and knowledge management
ADR-SPLDA: Activity discovery and recognition by combining sequential patterns and latent Dirichlet allocation

Pervasive and Mobile Computing
One click mining: interactive local pattern discovery through implicit preference and performance learning

Proceedings of the ACM SIGKDD Workshop on Interactive Data Exploration and Analytics

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.