Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Data preparation for data mining
Data preparation for data mining
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Advances in predictive models for data mining
Pattern Recognition Letters - Special issue on machine learning and data mining in pattern recognition
Efficient Mining of Association Rules in Distributed Databases
IEEE Transactions on Knowledge and Data Engineering
Parallel Mining of Association Rules
IEEE Transactions on Knowledge and Data Engineering
Toward Multidatabase Mining: Identifying Relevant Databases
IEEE Transactions on Knowledge and Data Engineering
Synthesizing High-Frequency Rules from Different Data Sources
IEEE Transactions on Knowledge and 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
Database classification for multi-database mining
Information Systems
Pattern Recognition Letters
Efficient classification from multiple heterogeneous databases
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Knowledge Discovery in Multiple Databases
Knowledge Discovery in Multiple Databases
Mining important association rules based on the RFMD technique
International Journal of Data Analysis Techniques and Strategies
Measuring influence of an item in a database over time
Pattern Recognition Letters
Rule synthesizing from multiple related databases
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
Clustering local frequency items in multiple databases
Information Sciences: an International Journal
Quality of information-based source assessment and selection
Neurocomputing
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Many large organizations have multiple databases distributed over different branches. Number of such organizations is increasing over time. Thus, it is necessary to study data mining on multiple databases. In this paper the following contributions are made: Firstly, an extended model is proposed for synthesizing global patterns from local patterns in different databases. Secondly, the notion of heavy association rule in multiple databases is introduced, and an algorithm for synthesizing such association rules in multiple databases is thus proposed. Thirdly, the notion of exceptional association rule in multiple databases is introduced, and an extension is made to the proposed algorithm to notify whether a heavy association rule is high-frequent or exceptional. We present experimental results on three real datasets. Also, we make a comparative analysis between the proposed algorithm and existing algorithm.