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
A tree projection algorithm for generation of frequent item sets
Journal of Parallel and Distributed Computing - Special issue on high-performance data mining
Database Mining: A Performance Perspective
IEEE Transactions on Knowledge and Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Pushing Support Constraints Into Association Rules Mining
IEEE Transactions on Knowledge and Data Engineering
Mining Top.K Frequent Closed Patterns without Minimum Support
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
On the Mining of Substitution Rules for Statistically Dependent Items
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
Efficient mining of both positive and negative association rules
ACM Transactions on Information Systems (TOIS)
Mining Frequent Itemsets without Support Threshold: With and without Item Constraints
IEEE Transactions on Knowledge and Data Engineering
Mining positive and negative association rules: an approach for confined rules
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
IMine: Index Support for Item Set Mining
IEEE Transactions on Knowledge and Data Engineering
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
The Cyclic Model Analysis on Sequential Patterns
IEEE Transactions on Knowledge and Data Engineering
ACM Transactions on Knowledge Discovery from Data (TKDD)
Mining multidimensional and multilevel sequential patterns
ACM Transactions on Knowledge Discovery from Data (TKDD)
Knowledge-Based Interactive Postmining of Association Rules Using Ontologies
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Knowledge and Data Engineering
Frequent regular itemset mining
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
PGLCM: Efficient Parallel Mining of Closed Frequent Gradual Itemsets
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
An information theoretic framework for data mining
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining association rules for label ranking
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part II
Item set mining based on cover similarity
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part II
What is Unequal among the Equals? Ranking Equivalent Rules from Gene Expression Data
IEEE Transactions on Knowledge and Data Engineering
Effective Pattern Discovery for Text Mining
IEEE Transactions on Knowledge and Data Engineering
An efficient GA-Based algorithm for mining negative sequential patterns
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Mining association rules in long sequences
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
| Hi-index | 12.05 |
Data mining has been studied for a long time. Its goal is to help market managers find relationships among items from large databases and thus increase sales volume. Association-rule mining is one of the well known and commonly used techniques for this purpose. The Apriori algorithm is an important method for such a task. Based on the Apriori algorithm, lots of mining approaches have been proposed for diverse applications. Many of these data mining approaches focus on positive association rules such as ''if milk is bought, then cookies are bought''. Such rules may, however, be misleading since there may be customers that buy milk and not buy cookies. This paper thus takes the properties of propositional logic into consideration and proposes an algorithm for mining highly coherent rules. The derived association rules are expected to be more meanful and reliable for business. Experiments on two datasets are also made to show the performance of the proposed approach.