Beyond market baskets: generalizing association rules to correlations
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
FARMER: finding interesting rule groups in microarray datasets
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
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Proceedings of the 9th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Mining Frequent Closed Patterns in Microarray Data
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Mining top-K covering rule groups for gene expression data
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
A Parameter-Free Associative Classification Method
DaWaK '08 Proceedings of the 10th international conference on Data Warehousing and Knowledge Discovery
A SVM-based discretization method with application to associative classification
Expert Systems with Applications: An International Journal
Rule Learning with Probabilistic Smoothing
DaWaK '09 Proceedings of the 11th International Conference on Data Warehousing and Knowledge Discovery
A study on interestingness measures for associative classifiers
Proceedings of the 2010 ACM Symposium on Applied Computing
Class association rule mining with multiple imbalanced attributes
AI'07 Proceedings of the 20th Australian joint conference on Advances in artificial intelligence
Combined association rule mining
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
CIMDS: adapting postprocessing techniques of associative classification for malware detection
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Predictive rule discovery from electronic health records
Proceedings of the 1st ACM International Health Informatics Symposium
Associative classification in the prediction of tuberculosis
Proceedings of the International Conference & Workshop on Emerging Trends in Technology
X-Class: Associative Classification of XML Documents by Structure
ACM Transactions on Information Systems (TOIS)
Editorial: Parameter-free classification in multi-class imbalanced data sets
Data & Knowledge Engineering
Behavior-based clustering and analysis of interestingness measures for association rule mining
Data Mining and Knowledge Discovery
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In this paper we propose CCCS, a new algorithm for classification based on association rule mining. The key innovation in CCCS is the use of a new measure, the "Complement Class Support (CCS)" whose application results in rules which are guaranteed to be positively correlated. Furthermore, the anti-monotonic property that CCS possesses has very different semantics vis-a-vis the traditional support measure. In particular, "good" rules have a low CCS value. This makes CCS an ideal measure to use in conjunction with a top-down algorithm. Finally, the nature of CCS allows the pruning of rules without the setting of any threshold parameter! To the best of our knowledge this is the first threshold-free algorithm in association rule mining for classification.