关联规则挖掘是数据挖掘中的重点研究内容之一。而在关联规则挖掘中又有多个不同的研究分支，其中按数据集中属性的类型可分为布尔型关联规则挖掘和数量型关联规则挖掘。对于布尔型挖掘人们已经进行了较多研究且取得了很多有价值的方法，而对于数量型关联规则挖掘的研究还有欠缺，因此本文主要针对数量型关联规则挖掘方法展开研究。 本文给出了BFCFPM算法挖掘模糊关联规则。该算法继承了FP_Growth算法中的递归挖掘思想，将其扩展应用到了模糊领域。BFCFPM算法主要思想是：首先使用FCM聚类技术将数量型数据集映射为模糊数据集，然后把模糊数据集压缩存储在IFFPT中，最后在IFFPT上递归地挖掘模糊频繁项集。该算法在提取模糊关联规则时有效地避免了因为属性离散化而造成的信息损失与规则的减少，同时减少了扫描数据集的次数。经测试该算法具有较好的性能。 本文又给出了FMFFI算法。其主要是针对BFCFPM算法在处理数据集记录数较多而项的个数较少时还需要建立IFFPT的数据存储问题。FMFFI算法的主要思想是：应用FCM聚类技术将数量型数据集映射为模糊数据集，在搜索模糊频繁项集时采用从高维向低维与从低维向高维双向的搜索方式。从低维到高维的搜索方式采用Apriori算法的思想，而从高维到低维的搜索时，是自最大模糊超集起，按次删除一个模糊1-项集来进行搜索。经测试，FMFFI算法同样具有较好的性能。

Association rules mining is one of the key research content in data mining. There are many different branches of study in association rule mining. The mining of association rules can be divided into two categories according to data attribute types, which are Boolean association rules mining and quantitative association rules mining. Researchers have do more research in Boolean association rule mining and gained a lot of valuable methods. But the study of mining quantitative association rules is still lacking. This paper mainly do some research in mining quantitative association rules. The algorithm has been extended to the fuzzy areas. This paper proposes a BFCFPM algorithm for mining fuzzy association rules, which inherits the recursive thought in FP_Growth algorithm. The main idea of BFCFPM algorithm is as follows. Firstly, mapping the quantitative data sets to fuzzy data sets by FCM clustering technology. Secondly, compress the fuzzy data sets and then store them to IFFPT. At last, recursively mining fuzzy frequent item sets on IFFPT. Loss of information and the rules coursed by discretization is avoided effectively when extracting fuzzy association rules in this algorithm. The algorithm has been tested and has good performance. Another FMFFI algorithm is proposed in this paper. The algorithm is mainly aimed to solute storage problems that BFCFPM algorithm establishes IFFPT when there are more data set records and fewer item number. FCM clustering technology is also be applied to mapping quantitative data sets to fuzzy data sets in this algorithm. When searching for fuzzy item sets, both ways from high-dimensional to low-dimensional and from low-dimensional to high-dimensional are applied. Searching from low-dimensional to high-dimensional adopts Apriori algorithm ,FMFFI algorithm also has good performance according to tests.