Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Introduction to data compression
Introduction to data compression
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Turbo-charging vertical mining of large databases
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Tree structure for efficient data mining using rough sets
Pattern Recognition Letters - Special issue: Rough sets, pattern recognition and data mining
Synthesizing High-Frequency Rules from Different Data Sources
IEEE Transactions on Knowledge and Data Engineering
Using Condensed Representations for Interactive Association Rule Mining
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
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
DBC: a condensed representation of frequent patterns for efficient mining
Information Systems
Database classification for multi-database mining
Information Systems
Generating a Condensed Representation for Association Rules
Journal of Intelligent Information Systems
Sequential Pattern Mining in Multi-Databases via Multiple Alignment
Data Mining and Knowledge Discovery
Mining Multiple Data Sources: Local Pattern Analysis
Data Mining and Knowledge Discovery
A logical framework for identifying quality knowledge from different data sources
Decision Support Systems
Data Structure for Association Rule Mining: T-Trees and P-Trees
IEEE Transactions on Knowledge and Data Engineering
Knowledge Discovery in Multiple Databases
Knowledge Discovery in Multiple Databases
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Multi-database mining using local pattern analysis could be considered as an approximate method of mining multiple large databases. Thus, it might be required to enhance the quality of knowledge synthesized from multiple databases. Also, many decision-making applications are directly based on the available local patterns in different databases. The quality of synthesized knowledge/decision based on local patterns in different databases could be enhanced by incorporating more local patterns in the knowledge synthesizing/processing activities. Thus, the available local patterns play a crucial role in building efficient multi-database mining applications. We represent patterns in condensed form by employing a coding called ACP coding. It allows us to consider more local patterns by lowering further the user inputs, like minimum support and minimum confidence. The proposed coding enables more local patterns participate in the knowledge synthesizing/processing activities and thus, the quality of synthesized knowledge based on local patterns in different databases gets enhanced significantly at a given pattern synthesizing algorithm and computing resource.