说明:双击或选中下面任意单词,将显示该词的音标、读音、翻译等;选中中文或多个词,将显示翻译。
您的位置:首页 -> 词典 -> 负频繁集
1)  Negative Frequent Itemset
负频繁集
2)  frequent itemsets
频繁项集
1.
Mining of maximum frequent itemsets and frequent closed itemsets based on frequent itemsets;
基于频繁项集挖掘最大频繁项集和频繁闭项集
2.
Algorithm of Mining Frequent Itemsets Based on Binary Representation;
基于二进制表示的频繁项集挖掘算法
3)  frequent item sets
频繁项集
1.
Algorithm for frequent item sets mining of sharing prefix based on FP-tree
基于FP-Tree的共享前缀频繁项集挖掘算法
2.
Algorithm for Mining Frequent Item Sets Based on Genetic Particle Swarm Optimization
基于遗传粒子群算法的频繁项集挖掘算法
3.
A new algorithm FIS-Miner(Frequent Item Sets Miner) is presented for discovering frequent item sets to decrease candidate generation based on vector matrix.
为减少冗余候选项集的产生,提出了一种基于向量矩阵的频繁项集挖掘算法FIS-Miner。
4)  frequent itemset
频繁项集
1.
Representation and mining of frequent itemsets based on the pruned concept lattice;
基于剪枝概念格模型的频繁项集表示及挖掘
2.
Representation and mining of frequent itemsets based on multiple pruned concept lattices;
基于多剪枝格的频繁项集表示与挖掘
3.
Efficient algorithm of mining weighted frequent itemsets based on matrix and bit string;
快速挖掘加权频繁项集的矩阵位串算法
5)  frequent items
频繁项集
1.
Applying the hierarchical sketch,an algorithm that finds hierarchical frequent items over data streams dynamically and approximately was implemented.
应用该多层概要数据结构,实现了面向数据流的多层频繁项集的动态近似查找算法。
2.
Due to the irreversibility of random hash mapping,current sketch data structures have to traverse the key address space to find frequent items.
由于随机哈希函数不可逆,目前的概要数据结构不得不遍历关键字地址空间以查找和估计频繁项集。
3.
It s been found the efficiency of algorithms can been improved by pruning the candidate items C_k based on frequent items L_(k-1),and ignoring the transactions which is useless for frequent items generated.
通过对关联规则产生过程的实际实验分析发现,可以采取利用频繁k-1项集Lk-1对候选k项集Ck进行预先剪枝、及在扫描数据库过程中忽略对频繁项集的产生无贡献的交易记录的方法来改进关联规则提取的效率。
6)  frequent item set
频繁项集
1.
Research on Mining Algorithms of Maximal Frequent Item Sets;
最大频繁项集挖掘算法的研究
2.
Making over the FP-tree structure,the frequent item set of mining algorithm called UPM is preposed.
在对FP-树进行改造的基础上提出基于划分思想的频繁项集挖掘算法UPM,算法的项集频度计算和非频繁项目裁剪都基于空间划分的思想。
3.
Aiming at the problem of data association rules mining about late years,this paper introduces vector inner product to the field and raises generating efficiency of database frequent item sets with equitable data store structure.
针对近年来研究较多的数据关联挖掘问题,论文将向量内积引入到该领域,并通过合理分配数据存储结构来提高数据库频繁项集的生成效率。
补充资料:负负
1.犹言惭愧﹑惭愧;对不起﹑对不起。
说明:补充资料仅用于学习参考,请勿用于其它任何用途。
参考词条