Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Fast discovery of association rules
Advances in knowledge discovery and data mining
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
An Efficient Algorithm for Mining Association Rules in Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Mining Generalized Association Rules
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
Treatment of Missing Values for Association Rules
PAKDD '98 Proceedings of the Second Pacific-Asia Conference on Research and Development in Knowledge Discovery and Data Mining
Representative Association Rules
PAKDD '98 Proceedings of the Second Pacific-Asia Conference on Research and Development in Knowledge Discovery and Data Mining
Mining Association Rules for Estimation and Prediction
PAKDD '98 Proceedings of the Second Pacific-Asia Conference on Research and Development in Knowledge Discovery and Data Mining
Legitimate Approach to Association Rules under Incompleteness
ISMIS '00 Proceedings of the 12th International Symposium on Foundations of Intelligent Systems
Mining itemsets in the presence of missing values
Proceedings of the 2007 ACM symposium on Applied computing
Journal of Intelligent Information Systems
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Discovering association rules among items in large databases is recognized as an important database mining problem. The problem has been introduced originally for sales transaction database and did not relate to missing data. However, missing data often occur in relational databases, especially in business ones. It is not obvious how to compute association rules t~om such incomplete databases. It is provided and proved in the paper how to estimate support and confidence of an association rule induced t~om an incomplete relational database. We also introduce definitions of expected support and confidence of an association rule. The proposed definitions guarantee some required properties of itemsets and association rules. Eventually, we discuss another approach to missing values based on so called valid databases and compare both approaches.