Data structures and algorithm analysis in C++
Data structures and algorithm analysis in C++
Efficient enumeration of frequent sequences
Proceedings of the seventh international conference on Information and knowledge management
A Graph-Based Approach for Discovering Various Types of Association Rules
IEEE Transactions on Knowledge and Data Engineering
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
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
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
Novel measurement for mining effective association rules
Knowledge-Based Systems
Mining association rules in very large clustered domains
Information Systems
Frequent pattern mining: current status and future directions
Data Mining and Knowledge Discovery
Normalised support: a virtual angle of measurement of 'interestingness'
International Journal of Data Analysis Techniques and Strategies
Research on automatic acquisition method of Chinese domain ontology backbone based on Hownet
International Journal of Wireless and Mobile Computing
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The task of data mining is to find the useful information within the incredible sets of data. One of important research areas of data mining is mining association rules. If we can find these relations by mining association rules, we can provide better selling strategy to gain more customers' attentions. However, in some applications, the large itemsets may not always correlate with each other. In this paper, we propose a new graph-based algorithm to discover the association rules. It represents the large itemsets as a graph, which constructs a graph based on L2. Then, by dividing the items to several groups, the association rule can be mined efficiently. We conduct several experiments using different synthetic transaction databases. The simulation results show that the GAR algorithm outperforms the FP-growth algorithm in the execution time for all transaction databases.