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首页> 外文会议>Business and Information Management, 2008. ISBIM '08 >Research on the FP Growth Algorithm about Association Rule Mining
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Research on the FP Growth Algorithm about Association Rule Mining

机译：关联规则挖掘的FP增长算法研究

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For large databases, the research on improving the mining performance and precision is necessary, so many focuses of today on association rule mining are about new mining theories, algorithms and improvement to old methods. Association rules mining is a function of data mining research domain and arise many researchers interest to design a high efficient algorithm to mine association rules from transaction database. Generally all the frequent item sets discovery from the database in the process of association rule mining shares of larger, the price is also spending more. This paper introduces an improved aprior algorithm so called FP-growth algorithm that will help resolve two neck-bottle problems of traditional apriori algorithm and has more efficiency than original one. In theoretic research, An anatomy of two representative arithmetics of the apriori and the FP growth explains the mining process of frequent patterns item set. The constructing method of FP tree structure is provided and how it affects association rule mining is discussed. Experimental results show that the algorithm has higher mining efficiency in execution time, memory usage and CPU utilization than most current ones like apriori.
机译：对于大型数据库，有必要进行有关提高挖掘性能和精度的研究，因此，当今有关关联规则挖掘的许多关注点都涉及新的挖掘理论，算法和对旧方法的改进。关联规则挖掘是数据挖掘研究领域的功能，引起了许多研究人员的兴趣，他们希望设计一种从交易数据库中挖掘关联规则的高效算法。通常所有频繁项集在关联规则挖掘过程中从数据库中发现份额较大，价格也花费更多。本文介绍了一种改进的先前算法，即FP-growth算法，它将有助于解决传统先验算法的两个瓶颈问题，并且比原始算法具有更高的效率。在理论研究中，先验和FP增长的两种代表性算法的解剖学解释了频繁模式项目集的挖掘过程。提供了FP树结构的构造方法，并讨论了其对关联规则挖掘的影响。实验结果表明，该算法在执行时间，内存使用率和CPU使用率方面比大多数现有算法（例如apriori）具有更高的挖掘效率。

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