Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Finding interesting rules from large sets of discovered association rules
CIKM '94 Proceedings of the third international conference on Information and knowledge management
Mining optimized gain rules for numeric attributes
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Knowledge Discovery from Telecommunication Network Alarm Databases
ICDE '96 Proceedings of the Twelfth 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
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
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Causality occupies a central position in human reasoning. It plays an essential role in commonsense decision-making. Data mining hopes to extract unsuspected information from very large databases. The results are inherently soft or fuzzy as the data is generally both incomplete and inexact. The best known data mining methods build rules. Association rules indicate the associative strength of data attributes. In many ways, the interest in association rules is that they seem to suggest causal, or at least, predictive relationships. Whether it can be said that any association rules express a causal relationship needs to be examined. In part, the utility of mined association rules depends on whether the rule is causal or coincidental. This paper explores some of the factors that impact causality in mined rules.