C4.5: programs for machine learning
C4.5: programs for machine learning
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Efficiently mining long patterns from databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Mining the most interesting rules
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Generating non-redundant association rules
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Constraint-Based Rule Mining in Large, Dense Databases
Data Mining and Knowledge Discovery
Current Approaches to Handling Imperfect Information in Data and Knowledge Bases
IEEE Transactions on Knowledge and Data Engineering
Concise Representation of Frequent Patterns Based on Disjunction-Free Generators
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Mining All Non-derivable Frequent Itemsets
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th 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
Approximate Association Rule Mining
Proceedings of the Fourteenth International Florida Artificial Intelligence Research Society Conference
Discovering Interesting Rules from Dense Data
Proceedings of the IIS'2002 Symposium on Intelligent Information Systems
Techniques for Dealing with Missing Values in Classification
IDA '97 Proceedings of the Second International Symposium on Advances in Intelligent Data Analysis, Reasoning about Data
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
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The problem of incomplete data in the data mining is well known. In the literature many solutions to deal with missing values in various knowledge discovery tasks were presented and discussed. In the area of association rules the problem was presented mainly in the context of relational data. However, the methods proposed for incomplete relational database can not be easily adapted to incomplete transactional data. In this paper we introduce postulates of a statistically justified approach to discovering rules from incomplete transactional data and present the new approach to this problem, satisfying the postulates.