An efficient strategy for mining exceptions in multi-databases

Authors:
Shichao Zhang;Chengqi Zhang;Jeffrey Xu Yu
Affiliations:
Faculty of Information Technology, University of Technology, Sydney, P.O. Box 123, Broadway, NSW 2007, Australia and Department of Systems Engineering and Engineering Management, Chinese Universit ...;Faculty of Information Technology, University of Technology, Sydney, P.O. Box 123, Broadway, NSW 2007, Australia;Department of Systems Engineering and Engineering Management, Chinese University of Hong Kong, Shatin, New Territories, Hong Kong
Venue:
Information Sciences: an International Journal
Year:
2004

Citing 10
Cited 2

Parallel mining algorithms for generalized association rules with classification hierarchy

SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Efficient search for association rules

Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Multidatabase Systems: An Advance Solution for Global Information Sharing

Multidatabase Systems: An Advance Solution for Global Information Sharing
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
Database Mining: A Performance Perspective

IEEE Transactions on Knowledge and Data Engineering
Synthesizing High-Frequency Rules from Different Data Sources

IEEE Transactions on Knowledge and Data Engineering
An Algorithm for Multi-relational Discovery of Subgroups

PKDD '97 Proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery
Peculiarity Oriented Multi-database Mining

PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Identifying Relevant Databases for Multidatabase Mining

PAKDD '98 Proceedings of the Second Pacific-Asia Conference on Research and Development in Knowledge Discovery and Data Mining

Sequential Pattern Mining in Multi-Databases via Multiple Alignment

Data Mining and Knowledge Discovery
Mining globally interesting patterns from multiple databases using kernel estimation

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.07

Visualization

Abstract

This paper proposes a new strategy, referred to as local instance analysis, for multidatabase mining. While many interstate organizations have an imperative need to analyze their data in multi-databases distributed throughout their branches, traditional multi-database mining utilizes the strategies for mono-database mining: pooling all the data from relevant databases into a single dataset for discovery. This leads to the destruction of useful information, for instance, '70% of branches within a company agreed that a married customer usually has at least 2 cars if his/her age is between 45 and 65'. This information assists in global decision-making within the company. Our new strategy is developed for discovering this useful information. Using the local instance analysis, we design an algorithm for identifying exceptions from multi-databases. Exceptional pattern reflects the 'individuality' of, say, branches of an interstate company.