Data mining, hypergraph transversals, and machine learning (extended abstract)
PODS '97 Proceedings of the sixteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Database design for incomplete relations
ACM Transactions on Database Systems (TODS)
Generating non-redundant association rules
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
A condensed representation to find frequent patterns
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Levelwise Search and Borders of Theories in KnowledgeDiscovery
Data Mining and Knowledge Discovery
Support Approximations Using Bonferroni-Type Inequalities
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
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
Approximate Association Rule Mining
Proceedings of the Fourteenth International Florida Artificial Intelligence Research Society Conference
An associative classifier based on positive and negative rules
Proceedings of the 9th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Mining positive and negative association rules: an approach for confined rules
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
KDID'05 Proceedings of the 4th international conference on Knowledge Discovery in Inductive Databases
Missing Values: Proposition of a Typology and Characterization with an Association Rule-Based Model
DaWaK '09 Proceedings of the 11th International Conference on Data Warehousing and Knowledge Discovery
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Missing values issue in databases is an important problem because missing values bias the information provided by the usual data mining methods. In this paper, we are searching for mining patterns satisfying correct properties in presence of missing values (it means that these patterns must satisfy the properties in the corresponding complete database). We focus on k-free patterns. Thanks to a new definition of this property suitable for incomplete data and compatible with the usual one, we certify that the extracted k-free patterns in an incomplete database also satisfy this property in the corresponding complete database. Moreover, this approach enables to provide an anti-monotone criterion with respect to the pattern inclusion and thus design an efficient level-wise algorithm which extracts correct k-free patterns in presence of missing values.