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
Scalable Algorithms for Association Mining
IEEE Transactions on Knowledge and Data Engineering
H-Mine: Hyper-Structure Mining of Frequent Patterns in Large Databases
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
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
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Frequent Pattern Mining using Bipartite Graph
DEXA '07 Proceedings of the 18th International Conference on Database and Expert Systems Applications
A Combination Approach to Frequent Itemsets Mining
ICCIT '08 Proceedings of the 2008 Third International Conference on Convergence and Hybrid Information Technology - Volume 01
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Association rule mining among frequent items has been widely studied in data mining field. Many researches have improved the algorithm for generation of all the frequent itemsets. In this paper, we proposed a new algorithm to mine all frequents itemsets from a transaction database. The main features of this paper are: (1) the database is scanned only one time to mine frequent itemsets; (2) the new algorithm called the JoinFI-Mine algorithm which use mathematics properties to reduces huge of subsequence mining; (3) the proposed algorithm mines frequent itemsets without generation of candidate sets; and (4) when the minimum support threshold is changed, the database is not require to scan. We have provided definitions, algorithms, examples, theorem, and correctness proving of the algorithm.