Proceedings of the sixth international workshop on Machine learning
Original Contribution: Stacked generalization
Neural Networks
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
An incremental concept formation approach for learning from databases
Theoretical Computer Science - Special issue on formal methods in databases and software engineering
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
Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Beyond market baskets: generalizing association rules to correlations
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
A new framework for itemset generation
PODS '98 Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Query flocks: a generalization of association-rule mining
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Parallel mining algorithms for generalized association rules with classification hierarchy
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Mining generalized association rules
Future Generation Computer Systems - Special double issue on data mining
Online association rule mining
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 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
BIG: an agent for resource-bounded information gathering and decision making
Artificial Intelligence - Special issue on Intelligent internet systems
Parallel data mining for association rules on shared memory systems
Knowledge and Information Systems
Distributed web log mining using maximal large item sets
Knowledge and Information Systems
Parallel and sequential algorithms for data mining using inductive logic
Knowledge and Information Systems
Cost complexity-based pruning of ensemble classifiers
Knowledge and Information Systems
Arbitrating among competing classifiers using learned referees
Knowledge and Information Systems
Applied Intelligence
A Survey of Methods for Scaling Up Inductive Algorithms
Data Mining and Knowledge Discovery
Data Mining: An Overview from a Database Perspective
IEEE Transactions on Knowledge and Data Engineering
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
Maintenance of Discovered Association Rules in Large Databases: An Incremental Updating Technique
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
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
A General Incremental Technique for Maintaining Discovered Association Rules
Proceedings of the Fifth International Conference on Database Systems for Advanced Applications (DASFAA)
Multi-layer Incremental Induction
PRICAI '98 Proceedings of the 5th Pacific Rim International Conference on Artificial Intelligence: Topics in Artificial Intelligence
An extensible meta-learning approach for scalable and accurate inductive learning
An extensible meta-learning approach for scalable and accurate inductive learning
Stacked generalization: when does it work?
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
An efficient strategy for mining exceptions in multi-databases
Information Sciences: an International Journal
Database classification for multi-database mining
Information Systems
Efficient Classification across Multiple Database Relations: A CrossMine Approach
IEEE Transactions on Knowledge and Data Engineering
Discovering Classification from Data of Multiple Sources
Data Mining and Knowledge Discovery
Fast Distributed Outlier Detection in Mixed-Attribute Data Sets
Data Mining and Knowledge Discovery
Sequential Pattern Mining in Multi-Databases via Multiple Alignment
Data Mining and Knowledge Discovery
Organizing Multiple Data Sources for Developing Intelligent e-Business Portals
Data Mining and Knowledge Discovery
Privacy preserving sequential pattern mining in distributed databases
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
A logical framework for identifying quality knowledge from different data sources
Decision Support Systems
Relational peculiarity-oriented mining
Data Mining and Knowledge Discovery
Enhancing quality of knowledge synthesized from multi-database mining
Pattern Recognition Letters
Synthesizing heavy association rules from different real data sources
Pattern Recognition Letters
An approach to mining bundled commodities
Knowledge-Based Systems
Modified algorithms for synthesizing high-frequency rules from different data sources
Knowledge and Information Systems
Multirelational classification: a multiple view approach
Knowledge and Information Systems
Mining globally interesting patterns from multiple databases using kernel estimation
Expert Systems with Applications: An International Journal
An Improved Database Classification Algorithm for Multi-database Mining
FAW '09 Proceedings of the 3d International Workshop on Frontiers in Algorithmics
Association rule mining: models and algorithms
Association rule mining: models and algorithms
Distributed learning with data reduction
Transactions on computational collective intelligence IV
CLAP: Collaborative pattern mining for distributed information systems
Decision Support Systems
Scalable inductive learning on partitioned data
ISMIS'05 Proceedings of the 15th international conference on Foundations of Intelligent Systems
Rule synthesizing from multiple related databases
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
Flexible online association rule mining based on multidimensional pattern relations
Information Sciences: an International Journal
Clustering local frequency items in multiple databases
Information Sciences: an International Journal
Mining stable patterns in multiple correlated databases
Decision Support Systems
Quality of information-based source assessment and selection
Neurocomputing
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Many large organizations have multiple data sources, such as different branches of an interstate company. While putting all data together from different sources might amass a huge database for centralized processing, mining association rules at different data sources and forwarding the rules (rather than the original raw data) to the centralized company headquarter provides a feasible way to deal with multiple data source problems. In the meanwhile, the association rules at each data source may be required for that data source in the first instance, so association analysis at each data source is also important and useful. However, the forwarded rules from different data sources may be too many for the centralized company headquarter to use. This paper presents a weighting model for synthesizing high-frequency association rules from different data sources. There are two reasons to focus on high-frequency rules. First, a centralized company headquarter is interested in high-frequency rules because they are supported by most of its branches for corporate profitability. Second, high-frequency rules have larger chances to become valid rules in the union of all data sources. In order to extract high-frequency rules efficiently, a procedure of rule selection is also constructed to enhance the weighting model by coping with low-frequency rules. Experimental results show that our proposed weighting model is efficient and effective.