Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Algorithm 457: finding all cliques of an undirected graph
Communications of the ACM
Parallel data mining for association rules on shared memory systems
Knowledge and Information Systems
Parallel and sequential algorithms for data mining using inductive logic
Knowledge and Information Systems
Parallel frequent set counting
Parallel Computing - Parallel data-intensive algorithms and applications
Efficient Mining of Association Rules in Distributed 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
Privacy preserving association rule mining in vertically partitioned data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Frequent Itemsets in Distributed and Dynamic Databases
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Communication-Efficient Distributed Mining of Association Rules
Data Mining and Knowledge Discovery
Knowledge Discovery in Multiple Databases
Knowledge Discovery in Multiple Databases
Ordering patterns by combining opinions from multiple sources
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Database classification for multi-database mining
Information Systems
Finding (Recently) Frequent Items in Distributed Data Streams
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
A logical framework for identifying quality knowledge from different data sources
Decision Support Systems
Synthesizing heavy association rules from different real data sources
Pattern Recognition Letters
Clustering local frequency items in multiple databases
Information Sciences: an International Journal
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In this paper, we study the problem of rule synthesizing from multiple related databases where items representing the databases may be different, and the databases may not be relevant, or similar to each other We argue that, for such multi-related databases, simple rule synthesizing without a detailed understanding of the databases is not able to reveal meaningful patterns inside the data collections Consequently, we propose a two-step clustering on the databases at both item and rule levels such that the databases in the final clusters contain both similar items and similar rules A weighted rule synthesizing method is then applied on each such cluster to generate final rules Experimental results demonstrate that the new rule synthesizing method is able to discover important rules which can not be synthesized by other methods.