Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
A new framework for itemset generation
PODS '98 Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
ACM Computing Surveys (CSUR)
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Data mining: concepts and techniques
Data mining: concepts and techniques
BIRCH: A New Data Clustering Algorithm and Its Applications
Data Mining and Knowledge Discovery
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
Toward Multidatabase Mining: Identifying Relevant Databases
IEEE Transactions on Knowledge and Data Engineering
Synthesizing High-Frequency Rules from Different Data Sources
IEEE Transactions on Knowledge and 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
Selecting the right interestingness measure for association patterns
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Fast and Robust General Purpose Clustering Algorithms
Data Mining and Knowledge Discovery
Knowledge Discovery in Multiple Databases
Knowledge Discovery in Multiple Databases
Database classification for multi-database mining
Information Systems
Elements of discrete mathematics (McGraw-Hill computer science series)
Elements of discrete mathematics (McGraw-Hill computer science series)
Synthesizing heavy association rules from different real data sources
Pattern Recognition Letters
Efficient clustering of databases induced by local patterns
Decision Support Systems
Mining conditional patterns in a database
Pattern Recognition Letters
Capturing association among items in a database
Data & Knowledge Engineering
Finding Maximal Fully-Correlated Itemsets in Large Databases
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
Efficient Discovery of Confounders in Large Data Sets
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
Developing Multi-Database Mining Applications
Developing Multi-Database Mining Applications
Study of select items in different data sources by grouping
Knowledge and Information Systems
A clustering algorithm for multiple data streams based on spectral component similarity
Information Sciences: an International Journal
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
Privacy-preserving hybrid collaborative filtering on cross distributed data
Knowledge and Information Systems
Data mining from multiple heterogeneous relational databases using decision tree classification
Pattern Recognition Letters
High utility pattern mining using the maximal itemset property and lexicographic tree structures
Information Sciences: an International Journal
Mining frequent patterns in a varying-size sliding window of online transactional data streams
Information Sciences: an International Journal
Clustering by analytic functions
Information Sciences: an International Journal
Optimal clustering in the context of overlapping cluster analysis
Information Sciences: an International Journal
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Frequent items could be considered as a basic type of patterns in a database. In the context of multiple data sources, most of the global patterns are based on local frequency items. A multi-branch company transacting from different branches often needs to extract global patterns from data distributed over the branches. Global decisions could be taken effectively using such patterns. Thus, it is important to cluster local frequency items in multiple databases. An overview of the existing measures of association is presented here. For the purpose of selecting the suitable technique of mining multiple databases, we have surveyed the existing multi-database mining techniques. A study on the related clustering techniques is also covered here. The notion of high frequency itemsets is introduced here, and an algorithm for synthesizing supports of such itemsets is designed. The existing clustering technique might cluster local frequency items at a low level, since it estimates association among items in an itemset with a low accuracy, and thus a new algorithm for clustering local frequency items is proposed. Due to the suitability of measure of association A"2, association among items in a high frequency itemset is synthesized based on it. The soundness of the clustering technique has been shown. Numerous experiments are conducted using five datasets, and the results on different aspects of the proposed problem are presented in the experimental section. The effectiveness of the proposed clustering technique is more visible in dense databases.