Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Artificial Intelligence in Medicine
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Medicine Paring Analysis is one of the most important tasks in the research of Traditional Chinese Medicine Prescriptions. The most essential and difficult step is to mine associations between different medicine items. This paper proposes an effective approach in solving this problem. The main contributions include: (1) proposing a novel data structure called indexed frequent pattern tree (IFPT) to maintain the mined frequent patterns (2) presenting an efficient algorithm called Nearest Neighbor First (NNF) to mine association rules from IFPT (3) designing and implementing two optimization strategies that avoid the examinations of a lot of subsets of Y that can't be the left part of any association rule of the form X $\Rightarrow$Y – X and thus achieving a wonderful performance and (4) conducting extensive experiments which show that NNF runs far faster than Apriori algorithm and has better scalability. And finally we demonstrate the effectiveness of this method in Medicine Paring Analysis.