Principles of database and knowledge-base systems, Vol. I
Principles of database and knowledge-base systems, Vol. I
C4.5: programs for machine learning
C4.5: programs for machine learning
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Unifying default reasoning and belief revision in a modal framework
Artificial Intelligence
From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
Fast discovery of association rules
Advances in knowledge discovery and data mining
Machine learning and data mining
Communications of the ACM
Unexpectedness as a measure of interestingness in knowledge discovery
Decision Support Systems - Special issue on WITS '97
Small is beautiful: discovering the minimal set of unexpected patterns
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
An iterative hypothesis-testing strategy for pattern discovery
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Building knowledge discovery-driven models for decision support in project management
Decision Support Systems
Answering constraint-based mining queries on itemsets using previous materialized results
Journal of Intelligent Information Systems
Rule interestingness analysis using OLAP operations
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Opportunity map: identifying causes of failure - a deployed data mining system
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Data-based revision of probability distributions in qualitative multi-attribute decision models
Intelligent Data Analysis
Mining product maps for new product development
Expert Systems with Applications: An International Journal
Explaining clinical decisions by extracting regularity patterns
Decision Support Systems
Mining customer knowledge for product line and brand extension in retailing
Expert Systems with Applications: An International Journal
GAM: a guidance enabled association mining environment
International Journal of Business Intelligence and Data Mining
A method for mining quantitative association rules
SMO'06 Proceedings of the 6th WSEAS International Conference on Simulation, Modelling and Optimization
Mining stock category association and cluster on Taiwan stock market
Expert Systems with Applications: An International Journal
Mining marketing maps for business alliances
Expert Systems with Applications: An International Journal
Measuring interestingness of discovered skewed patterns in data cubes
Decision Support Systems
Mining information users' knowledge for one-to-one marketing on information appliance
Expert Systems with Applications: An International Journal
Mining demand chain knowledge of life insurance market for new product development
Expert Systems with Applications: An International Journal
Ontology-based data mining approach implemented for sport marketing
Expert Systems with Applications: An International Journal
ICCS 2009 Proceedings of the 9th International Conference on Computational Science
Discovery of unapparent association rules based on extracted probability
Decision Support Systems
Mining customer knowledge to implement online shopping and home delivery for hypermarkets
Expert Systems with Applications: An International Journal
Mining customer knowledge for direct selling and marketing
Expert Systems with Applications: An International Journal
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In prior work, we provided methods that generate unexpected patterns with respect to managerial intuition by eliciting managers' beliefs about the domain and using these beliefs to seed the search for unexpected patterns in data. Unexpected patterns discovered in this manner represent contradictions or "holes" in domain knowledge which need to be resolved. Given a belief and a set of unexpected patterns, the motivation behind knowledge refinement is that the belief can be made stronger by refining the belief based on the discovered patterns. In this paper we address the problem of incorporating the discovered contradictions into the belief system based on a formal logic approach. Specifically, we present a framework for refinement based on a generic knowledge refinement strategy, describe abstract properties of refinement algorithms that can be used to compare specific instantiations and then describe and compare two specific refinement algorithms based on this framework.