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 effective hash-based algorithm for mining association rules
SIGMOD '95 Proceedings of the 1995 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
Mining association rules with multiple minimum supports
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Data mining: concepts and techniques
Data mining: concepts and techniques
TBAR: An efficient method for association rule mining in relational databases
Data & Knowledge Engineering
A fast distributed algorithm for mining association rules
DIS '96 Proceedings of the fourth international conference on on Parallel and distributed information systems
Mining association rules using inverted hashing and pruning
Information Processing Letters
Mining Multiple-Level Association Rules in Large Databases
IEEE Transactions on Knowledge and Data Engineering
Database Mining: A Performance Perspective
IEEE Transactions on Knowledge and Data Engineering
Share Based Measures for Itemsets
PKDD '97 Proceedings of the First European Symposium 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
Sampling Large Databases for Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Information Sciences—Informatics and Computer Science: An International Journal
Expert Systems with Applications: An International Journal
Mining association rules in very large clustered domains
Information Systems
Adaptive estimated maximum-entropy distribution model
Information Sciences: an International Journal
An efficient algorithm for mining frequent inter-transaction patterns
Information Sciences: an International Journal
Discovering frequent itemsets by support approximation and itemset clustering
Data & Knowledge Engineering
MRM: A matrix representation and mapping approach for knowledge acquisition
Knowledge-Based Systems
Efficient single-pass frequent pattern mining using a prefix-tree
Information Sciences: an International Journal
FIUT: A new method for mining frequent itemsets
Information Sciences: an International Journal
Sliding window-based frequent pattern mining over data streams
Information Sciences: an International Journal
Mining High-Correlation Association Rules for Inferring Gene Regulation Networks
DaWaK '09 Proceedings of the 11th International Conference on Data Warehousing and Knowledge Discovery
Information Sciences: an International Journal
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part I
Advanced Matrix Algorithm (AMA): reducing number of scans for association rule generation
International Journal of Business Intelligence and Data Mining
Design and implementation of an intelligent automatic question answering system based on data mining
ICSI'12 Proceedings of the Third international conference on Advances in Swarm Intelligence - Volume Part II
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Conventional algorithms for mining association rules operate in a combination of smaller large itemsets. This paper presents a new efficient which combines both the cluster concept and decomposition of larger candidate itemsets, while proceeds from mining the maximal large itemsets down to large 1-itemsets, named cluster-decomposition association rule (CDAR). First, the CDAR method creates some clusters by reading the database only once, and then clustering the transaction records to the kth cluster, where the length of a record is k. Then, the large k-itemsets are generated by contrasts with the kth cluster only, unlike the combination concept that contrasts with the entire database. Experiments with real-life databases show that CDAR outperforms Apriori, a well-known and widely used association rule.