Fast discovery of association rules
Advances in 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
Communications of the ACM - Supporting community and building social capital
An alternative approach to computing with words
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Selecting the right interestingness measure for association patterns
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Top.K Frequent Closed Patterns without Minimum Support
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
On Incorporating Subjective Interestingness Into the Mining Process
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Mining unexpected rules by pushing user dynamics
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Rule interestingness analysis using OLAP operations
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Extracting redundancy-aware top-k patterns
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Computing with words and its relationships with fuzzistics
Information Sciences: an International Journal
Semantic Computing
Formal Concept Analysis: foundations and applications
Formal Concept Analysis: foundations and applications
Fuzzy logic = computing with words
IEEE Transactions on Fuzzy Systems
Feature Based Rule Learner in Noisy Environment Using Neighbourhood Rough Set Model
International Journal of Software Science and Computational Intelligence
Cognitive Memory: Human Like Memory
International Journal of Software Science and Computational Intelligence
On Cognitive Models of Causal Inferences and Causation Networks
International Journal of Software Science and Computational Intelligence
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Association mining aims to find valid correlations among data attributes, and has been widely applied to many areas of data analysis. This paper presents a semantic network-based association analysis model including three spreading activation methods. It applies this model to assess the quality of a dataset, and generate semantically valid new hypotheses for adaptive study design especially useful in medical studies. The approach is evaluated on a real public health dataset, the Heartfelt study, and the experiment shows promising results.