fmf meaning in Chinese
胎动感
原唱手机铃声
Examples
- Southwestern journal of anthropology , 1954 , 10 : 1 - 43 . 25 ucinet iv datasets . http : vlado . fmf . uni - lj . si pub net - works data ucinet ucidata . htmsampson
但是通过试验，我们发现mcs算法对于多数基准数据集都能找到好的解。 - The second order method of fmf is more efficient than the first one , and the advantage will be more obvious with support limit lower . the ooa mining hopes to mine all association rules based on a given objective , support , confidence and utility
( 2 )面向目标的基于效用度的关联规则挖掘( ooa挖掘)是在给定目标的情况下,挖掘满足支持度、置信度和效用度阈值的规则。 - The main contributions of this paper are as follows : we present an efficient algorithm for mining fuzzy frequent itemsets , called fmf . we use ffp tree structure to store frequent item sets imformation , and store ids of transactions related with fuzzy item in tree nodes . in fmf , we can count a fuzzy itemsets support through finding all trasactions including them . we needn ’ t to scan database all . to generate itemset { a } + x ( i . e
本文的主要工作如下: ( 1 )针对模糊频繁集的挖掘问题,提出了一种有效的fmf算法,在该算法中采用ffp -树结构,将与模糊项目相关的事务的序号保存到树结点中。计算一个模糊项集的支持度,可以通过直接找到所有包含该项集的全部事务进行计算,而不必扫描整个数据库。 - Super set of iemset x ) according to constrained subtree of itemset x , if item “ a ” isn ’ t a fuzzy item , we don ’ t scan database in addition . it can be generated by ffp tree . we propose two order methods for constructing ffp tree . one is that sorting database attributes holding frequent item in ascending order of their nodes number in ffp tree . another is that sorting frequent item of not fuzzy attributes in descending order of their support firstly , then sorting database fuzzy attributes with frequent item in ascending order of their nodes number in ffp tree . our experimental results show that although fmf needs more space costly than the algorithms based on apriori , its time costly is obviously lower than the latter
针对ffp -树的生成,提出了两种排序方法:按属性顺序将每个属性下的频繁项目依次插入到头表中,属性按照其在ffp -树中可能的不同结点的个数从少到多进行排序;先对非模糊属性下的频繁项目按支持度从大到小进行排序,再对模糊属性按其在ffp -树中包含的不同结点的个数,从少到多进行排序,然后依次将各属性下的频繁项目插入到头表中。