Unknown attribute values in induction
Proceedings of the sixth international workshop on Machine learning
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
An effective hash-based algorithm for mining association rules
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
The KDD process for extracting useful knowledge from volumes of data
Communications of the ACM
Association rules over interval data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Knowledge discovery preprocessing: determining record usability
ACM-SE 36 Proceedings of the 36th annual Southeast regional conference
Data mining: concepts and techniques
Data mining: concepts and techniques
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
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
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
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
Knowledge management performance evaluation: a decade review from 1995 to 2004
Journal of Information Science
Finding nuggets in documents: A machine learning approach
Journal of the American Society for Information Science and Technology
Perfect Hashing Schemes for Mining Association Rules
The Computer Journal
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
Mining rules from an incomplete dataset with a high missing rate
Expert Systems with Applications: An International Journal
Optimum estimation of missing values in randomized complete block design by genetic algorithm
Knowledge-Based Systems
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With the rapid increase in the use of databases, the problem of missing values inevitably arises. The techniques developed to recover these missing values effectively should be highly precise in order to estimate the missing values completely. The mining of association rules can effectively establish the relationship among items in databases. Therefore, discovered association rules are usually applied to recover the missing values in databases. This study presents a Fast Recycle Combined Association Rules (FRCAR) method to fill in the missing values. FRCAR applies a technique to recycle sub-frequent itemsets and bit-arrays to discover more association rules than the Missing Value Completion (MVC) approach. The experimental result demonstrates that FRCAR results in a higher recovery rate and higher recovery accuracy for missing values.