Efficient parallel data mining for association rules
CIKM '95 Proceedings of the fourth international conference on Information and knowledge management
An effective hash-based algorithm for mining association rules
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Applying on-line bitmap indexing to reduce counting costs in mining association rules
Information Sciences—Informatics and Computer Science: An International Journal
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
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
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
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Data mining aims to efficiently discover previously unknown knowledge from large databases. It is highly demanding in numerous real-life applications, such as marketing strategy, financial forecast, etc. One of the fundamental problems in the area is the efficient computation of association rules. In this paper, we shall investigate this problem in a distributed database. Particularly, we will present an efficient distributed algorithm for mining distributed association rules. Our experiment results suggest that the proposed algorithm outperforms the existing distributed algorithms. Further, we also study a distributed version of the problem of association rules; and extend our algorithm to solve this new problem.