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
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 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
Discovering data mining: from concept to implementation
Discovering data mining: from concept to implementation
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
Data Mining: An Overview from a Database Perspective
IEEE Transactions on Knowledge and Data Engineering
Mining Multiple-Level Association Rules in Large Databases
IEEE Transactions on Knowledge and Data Engineering
ICDE '95 Proceedings of the Eleventh 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
Mining Generalized Association Rules
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
Expert Systems with Applications: An International Journal
BitTableFI: An efficient mining frequent itemsets algorithm
Knowledge-Based Systems
An approach to mining bundled commodities
Knowledge-Based Systems
Towards personalized recommendation by two-step modified Apriori data mining algorithm
Expert Systems with Applications: An International Journal
Index-BitTableFI: An improved algorithm for mining frequent itemsets
Knowledge-Based Systems
CBP: A New Efficient Method for Mining Multilevel and Generalized Frequent Itemsets
ADMA '08 Proceedings of the 4th international conference on Advanced Data Mining and Applications
FIUT: A new method for mining frequent itemsets
Information Sciences: an International Journal
Discovery of unapparent association rules based on extracted probability
Decision Support Systems
RMAIN: Association rules maintenance without reruns through data
Information Sciences: an International Journal
New Classification Method Based on Support-Significant Association Rules Algorithm
ICIC '07 Proceedings of the 3rd International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence
Expert Systems with Applications: An International Journal
A modified fuzzy c-means algorithm for association rules clustering
ICIC'06 Proceedings of the 2006 international conference on Intelligent computing: Part II
Advanced Matrix Algorithm (AMA): reducing number of scans for association rule generation
International Journal of Business Intelligence and Data Mining
Web usage mining for the recommendation of materialized webviews
Proceedings of the 12th International Conference on Information Integration and Web-based Applications & Services
Designing of dynamic labor inspection system for construction industry
Expert Systems with Applications: An International Journal
Applying cluster-based fuzzy association rules mining framework into EC environment
Applied Soft Computing
Efficient colossal pattern mining in high dimensional datasets
Knowledge-Based Systems
Improving the performance of association classifiers by rule prioritization
Knowledge-Based Systems
MOWS: macro and micro online webview selection
Proceedings of the 3rd International Conference on Web Intelligence, Mining and Semantics
Hi-index | 0.01 |
The discovery of association rules is an important data-mining task for which many algorithms have been proposed. However, the efficiency of these algorithms needs to be improved to handle real-world large datasets. In this paper, we present an efficient algorithm named cluster-based association rule (CBAR). The CBAR method is to create cluster tables by scanning the database once, and then clustering the transaction records to the k-th cluster table, where the length of a record is k. Moreover, the large itemsets are generated by contrasts with the partial cluster tables. This not only prunes considerable amounts of data reducing the time needed to perform data scans and requiring less contrast, but also ensures the correctness of the mined results. Experiments with the FoodMart transaction database provided by Microsoft SQL Server show that CBAR outperforms Apriori, a well-known and widely used association rule.