FP-growth算法C语言实现

摘要：
事务数据库中频繁模式的挖掘研究作为关联规则等许多数据挖掘问题的核心工作，已经研究了许多年。早期算法大都是Apriori型算法，即首先产生候选集，然后在候选集的基础上找出频繁模式，候选集的产生往往是耗时的，特别是挖掘富模式或长模式时。后提出了一种新颖的数据结构FP-tree 及基于其上的FP-growth算法，用于有效的富模式与长模式挖掘。本文在研究Apriori算法的理论后，着重研究FP-growth算法用C语言实现的过程。

关键词：频繁模式；关联规则；数据挖掘；Apriori型算法；FP-growth算法；
Abstract:

Mining frequent patterns in transaction databases, as an essential role in many data mining tasks such as the association rule mining, has been widely studied for many years. Most of the previous studies adopt an Apriori-like candidate set generation-and-test approach. However , candidate set generation is costly if there exist prolific patterns or long patterns. After one propose a frequent pattern tree structure and a FP-frowth algorithm based on this structure that can mine the frequent patterns by pattern fragment growth. In this paper, firstly studied Apriori candidate set, and then studied FP-frowth algorithm and used C algorithm to realize the process.

Keywords: Frequent Pattern; Association Rule; Data Mining; Apriori Algorthm; FP-growth

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