C4.5: programs for machine learning
C4.5: programs for machine learning
Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
ACM Computing Surveys (CSUR)
From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
From contingency tables to various forms of knowledge in databases
Advances in knowledge discovery and data mining
Attribute-oriented induction in data mining
Advances in knowledge discovery and data mining
Selecting and reporting what is interesting
Advances in knowledge discovery and data mining
From data mining to knowledge discovery: current challenges and future directions
Advances in knowledge discovery and data mining
Feature Extraction, Construction and Selection: A Data Mining Perspective
Feature Extraction, Construction and Selection: A Data Mining Perspective
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
An Information Theoretic Approach to Rule Induction from Databases
IEEE Transactions on Knowledge and Data Engineering
Feature Selection via Discretization
IEEE Transactions on Knowledge and Data Engineering
Machine Learning
Data-Driven Discovery of Quantitative Rules in Relational Databases
IEEE Transactions on Knowledge and Data Engineering
Database Mining: A Performance Perspective
IEEE Transactions on Knowledge and Data Engineering
Discovery of Multiple-Level Association Rules from Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Proposed interestingness measure for characteristic rules
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Post-analysis of learned rules
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Database classification for multi-database mining
Information Systems
CoLe: A Cooperative Data Mining Approach and Its Application to Early Diabetes Detection
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Synthesizing heavy association rules from different real data sources
Pattern Recognition Letters
Efficient clustering of databases induced by local patterns
Decision Support Systems
Data mining research for customer relationship management systems: a framework and analysis
International Journal of Business Information Systems
Modified algorithms for synthesizing high-frequency rules from different data sources
Knowledge and Information Systems
An Improved Database Classification Algorithm for Multi-database Mining
FAW '09 Proceedings of the 3d International Workshop on Frontiers in Algorithmics
Mining important association rules based on the RFMD technique
International Journal of Data Analysis Techniques and Strategies
A cooperative multi-agent data mining model and its application to medical data on diabetes
AIS-ADM 2005 Proceedings of the 2005 international conference on Autonomous Intelligent Systems: agents and Data Mining
Clustering local frequency items in multiple databases
Information Sciences: an International Journal
Improving the efficiency of distributed data mining using an adjustment work flow
MLDM'13 Proceedings of the 9th international conference on Machine Learning and Data Mining in Pattern Recognition
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Various tools and systems for knowledge discovery and data mining are developed and available for applications. However, when we are immersed in heaps of databases, an immediate question is where we should start mining. It is not true that the more databases, the better for data mining. It is only true when the databases involved are relevant to a task at hand. In this paper, breaking away from the conventional data mining assumption that many databases be joined into one, we argue that the first step for multidatabase mining is to identify databases that are most likely relevant to an application; without doing so, the mining process can be lengthy, aimless, and ineffective. A measure of relevance is thus proposed for mining tasks with an objective of finding patterns or regularities about certain attributes. An efficient algorithm for identifying relevant databases is described. Experiments are conducted to verify the measure's performance and to exemplify its application.