Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Beyond market baskets: generalizing association rules to correlations
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Entropy-based subspace clustering for mining numerical data
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Detecting Group Differences: Mining Contrast Sets
Data Mining and Knowledge Discovery
Alternative Interest Measures for Mining Associations in Databases
IEEE Transactions on Knowledge and Data Engineering
Mining border descriptions of emerging patterns from dataset pairs
Knowledge and Information Systems
Mining quantitative correlated patterns using an information-theoretic approach
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Data Clustering: Theory, Algorithms, and Applications (ASA-SIAM Series on Statistics and Applied Probability)
Mining non-redundant high order correlations in binary data
Proceedings of the VLDB Endowment
The Journal of Machine Learning Research
DS'10 Proceedings of the 13th international conference on Discovery science
Top-N minimization approach for indicative correlation change mining
MLDM'12 Proceedings of the 8th international conference on Machine Learning and Data Mining in Pattern Recognition
| Hi-index | 0.00 |
Contrast set mining has been well-studied to detect the change between several contrasted databases. In the previous studies, they compared the supports of an itemset and extracted the itemsets with significantly different supports across those databases. Differently, we contrast the correlations of an itemset between two contrasted databases and try to detect potential changes. Any highly correlated itemset is out of our concern in order to focus on implicitly emerging correlation. Therefore, we set correlation constraints (upper bounds) in both databases, and then extract the itemsets consisting of items that are not highly correlated in both databases, but having a significant change of correlations from one database to the other. We regard both of positive and negative correlation. We also consider correlated itemsets under conditioning by third variables. Thus so called partial correlation is also regarded. To cover the correlation notion, we use extended mutual information. In our search procedure for the correlated itemsets, we use double clique condition that is necessary for itemsets to be solutions satisfying the correlation constraints. We show its usefulness by some experiments.