Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Efficiently mining long patterns from databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
SLIQ: A Fast Scalable Classifier for Data Mining
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
A Tightly-Coupled Architecture for Data Mining
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
A New SQL-like Operator for Mining Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
A unified approach for discovery of interesting association rules in medical databases
ICDM'06 Proceedings of the 6th Industrial Conference on Data Mining conference on Advances in Data Mining: applications in Medicine, Web Mining, Marketing, Image and Signal Mining
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The user interested in mining a data set by means of the extraction of association rules has to formulate mining queries or meta-patterns for association rule mining, which specify the features of the particular data mining problem. In this paper, we propose an exploration technique for the discovery of association rule meta-patterns able to extract quality rule sets, i.e. association rule sets which are meaningful and useful for the user. The proposed method is based on simple heuristic analysis techniques, suitable for an efficient preliminary analysis performed before applying the computationally expensive techniques for mining association rules.