论文信息 - An Improved FP-Growth Algorithm and its Application in Tongue Diagnosis of Traditional Chinese Medicine

An Improved FP-Growth Algorithm and its Application in Tongue Diagnosis of Traditional Chinese Medicine

At the beginning, the paper analyzed the characteristics of clinical diseases database in the Traditional Chinese Medicine (TCM) tongue diagnosis. Secondly, the paper presented improved FP-growth mining algorithm, which was usable to extract the diagnosis rules. The algorithm was realized by using VC++ 7.0 and SQL Server 2000 as the development tools, and applied it to the case database successfully. The application result make clear that the algorithm can be accurate more and fitter.

Chen Qun | Wei Yuke | Liu Mei | Li Jiangping | Hu Zhibing

[1] Zhang Jing. Representation and mining of frequent itemsets based on multiple pruned concept lattices , 2007 .

[2] Ramakrishnan Srikant,et al. Fast Algorithms for Mining Association Rules in Large Databases , 1994, VLDB.

[3] Ramakrishnan Srikant,et al. Mining generalized association rules , 1995, Future Gener. Comput. Syst..

[4] Jian Pei,et al. Mining frequent patterns without candidate generation , 2000, SIGMOD '00.

[5] Jian Pei,et al. Mining frequent patterns without candidate generation , 2000, SIGMOD 2000.

[6] R. Suganya,et al. Data Mining Concepts and Techniques , 2010 .

[7] Hannu Toivonen,et al. Sampling Large Databases for Association Rules , 1996, VLDB.

[8] Qiu Jiang-tao. Efficient algorithm for incremental updating of frequent itemsets based on FP-tree , 2007 .