An effective hash-based algorithm for mining association rules
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Mining fuzzy association rules in databases
ACM SIGMOD Record
Efficiently mining long patterns from databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 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
Maintenance of Discovered Association Rules in Large Databases: An Incremental Updating Technique
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
Discovering Frequent Closed Itemsets for Association Rules
ICDT '99 Proceedings of the 7th International Conference on Database Theory
PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth
Proceedings of the 17th International Conference on Data Engineering
Efficiently Mining Maximal Frequent Itemsets
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
An Efficient Algorithm for Mining Association Rules in Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Sampling Large Databases for Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
SmartMiner: A Depth First Algorithm Guided by Tail Information for Mining Maximal Frequent Itemsets
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
CLOSET+: searching for the best strategies for mining frequent closed itemsets
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
IEEE Transactions on Knowledge and Data Engineering
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Spirit diagnosing is an important theory in TCM Traditional Chinese Medicine, by which a TCM doctor can diagnose a patient's body state. But this theory is complicated and difficult to master simply learned from books. To further the theory and skill of spirit diagnosing, in this paper, the authors propose a remote education system that can accept videos from a user and give the user an auto-diagnosed spirit. The key technology in this system is eye feature computation in spirit diagnosing, for which rules describing "the spirit" spirit in TCM refers to the human's mental state which reflects the one's general physical condition state are mined by the quantitative features regarding the human eyes. With videos capturing eye condition during a short period, a set of eye features are extracted. On this basis, attribute intervals of the eye feature space is generated by CAIM class-attribute interdependence maximization. Several of the candidate rules are then mined by the association rule based on the cloud model. Finally, three complementary rule-pruning methods are modified and combined to trim the candidate rules. The cross validation test for mined rules has an average accuracy of 93%, which shows the high performance of the proposed method.