A mechanical solution of Schubert's steamroller by many-sorted resolution
Artificial Intelligence
A more expressive formulation of many sorted logic
Journal of Automated Reasoning
Finding interesting rules from large sets of discovered association rules
CIKM '94 Proceedings of the third international conference on Information and knowledge management
Formal ontology, conceptual analysis and knowledge representation
International Journal of Human-Computer Studies - Special issue: the role of formal ontology in the information technology
Toward principles for the design of ontologies used for knowledge sharing
International Journal of Human-Computer Studies - Special issue: the role of formal ontology in the information technology
Beyond market baskets: generalizing association rules to correlations
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
New algorithms for efficient mining of association rules
Information Sciences: an International Journal
Generating non-redundant association rules
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Small is beautiful: discovering the minimal set of unexpected patterns
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
TBAR: An efficient method for association rule mining in relational databases
Data & Knowledge Engineering
Ontology-guided knowledge discovery in databases
Proceedings of the 1st international conference on Knowledge capture
An Ontology for Quality Management — Enabling Quality Problem Identification and Tracing
BT Technology Journal
Description Logics in Data Management
IEEE Transactions on Knowledge and Data Engineering
Data Mining: An Overview from a Database Perspective
IEEE Transactions on Knowledge and Data Engineering
What Makes Patterns Interesting in Knowledge Discovery Systems
IEEE Transactions on Knowledge and Data Engineering
Analyzing the Subjective Interestingness of Association Rules
IEEE Intelligent Systems
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
An Efficient Algorithm for Mining Association Rules in Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Exception Rule Mining with a Relative Interestingness Measure
PADKK '00 Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications
A note on "beyond market baskets: generalizing association rules to correlations"
ACM SIGKDD Explorations Newsletter
Ontologies: A Silver Bullet for Knowledge Management and Electronic Commerce
Ontologies: A Silver Bullet for Knowledge Management and Electronic Commerce
Knowledge pruning in decision trees
ICTAI '00 Proceedings of the 12th IEEE International Conference on Tools with Artificial Intelligence
A predicate-ordered logic for knowledge representation on the web
Future Generation Computer Systems - Special issue: Semantic grid and knowledge grid: the next-generation web
Selecting the right objective measure for association analysis
Information Systems - Knowledge discovery and data mining (KDD 2002)
Ontologies for Knowledge Management: An Information Systems Perspective
Knowledge and Information Systems
Mining Frequent Itemsets without Support Threshold: With and without Item Constraints
IEEE Transactions on Knowledge and Data Engineering
TFP: An Efficient Algorithm for Mining Top-K Frequent Closed Itemsets
IEEE Transactions on Knowledge and Data Engineering
Supporting knowledge-intensive inspection tasks with application ontologies
International Journal of Human-Computer Studies
Automatic discovery of locally frequent itemsets in the presence of highly frequent itemsets
Intelligent Data Analysis
A knowledge engineering approach to knowledge management
Information Sciences: an International Journal
Domain ontology driven data mining: a medical case study
Proceedings of the 2007 international workshop on Domain driven data mining
HICSS '08 Proceedings of the Proceedings of the 41st Annual Hawaii International Conference on System Sciences
On discovery of soft associations with "most" fuzzy quantifier for item promotion applications
Information Sciences: an International Journal
An ontology-based approach to learnable focused crawling
Information Sciences: an International Journal
Efficient single-pass frequent pattern mining using a prefix-tree
Information Sciences: an International Journal
Learning and inferencing in user ontology for personalized Semantic Web search
Information Sciences: an International Journal
Reducing OWL entailment to description logic satisfiability
Web Semantics: Science, Services and Agents on the World Wide Web
The Knowledge Engineering Review
Ontology-based concept similarity in Formal Concept Analysis
Information Sciences: an International Journal
How to visualize a crisp or fuzzy topic set over a taxonomy
PReMI'11 Proceedings of the 4th international conference on Pattern recognition and machine intelligence
Conceptual distance for association rules post-processing
MEDI'11 Proceedings of the First international conference on Model and data engineering
Tag co-occurrence analysis using the association data mining rule
Proceedings of the 2012 iConference
Generalized association rule mining with constraints
Information Sciences: an International Journal
An iterative approach to build relevant ontology-aware data-driven models
Information Sciences: an International Journal
Information Sciences: an International Journal
Mining association rules for the quality improvement of the production process
Expert Systems with Applications: An International Journal
Extensible access control markup language integrated with Semantic Web technologies
Information Sciences: an International Journal
Fuzzy partitions: A way to integrate expert knowledge into distance calculations
Information Sciences: an International Journal
A combined mining-based framework for predicting telecommunications customer payment behaviors
Expert Systems with Applications: An International Journal
Review: Knowledge discovery in medicine: Current issue and future trend
Expert Systems with Applications: An International Journal
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Data mining is used to discover hidden patterns or structures in large databases. Association rule induction extracts frequently occurring patterns in the form of association rules. However, this technique has a drawback as it typically generates a large number of association rules. Several methods have been proposed to prune the set of extracted rules in order to present only those which are of interest to the domain experts. Some of these methods involve subjective analysis based on prior domain knowledge, while others can be considered to involve objective, data-driven analysis based on numerical measures that provide a partial description of the interestingness of the extracted association rules. Recently it has been proposed that ontologies could be used to guide the data mining process. In this paper, we propose a hybrid pruning method that involve the use of objective analysis and subjective analysis, with the latter involving the use of an ontology. We demonstrate the applicability of this hybrid method using a medical database.