频繁项集

频繁项集；挖掘；支持度；查询扩展；

Frequent itemset Mining Support Query expansion ;

提高频繁项集挖掘算法的效率是关联规则挖掘研究的一个重点领域。

Enhancing the efficiency of frequent itemset mining algorithm is an important area of researching association rule mining .

针对文本关联分析中难以确定最小支持度阈值的问题，提出N个最频繁项集挖掘算法。

Research on mining the top N most frequent item sets in text collection .

同Apriori算法相比较，该算法能直接查找高次频繁项集，可以有效地屏蔽Apriori算法性能瓶颈。

This algorithm can effectively resolve the bottleneck of Apriori .

挖掘关联规则频繁项集的算法研究及其Prolog实现

Research and Realization Using Prolog on Mining Frequent Items Algorithm of Association Rules

与CSR相比，算法CR能够缩小频繁项集的候选集的规模，从而提高算法的效率，并且算法CR中的压缩数据库的结构也较算法CSR中压缩数据库的结构更为简练，节省了空间。

It has more efficiency , for it has a better structure in the compressed database , and reduces the scale of frequent item sets .

基于FP-Tree的共享前缀频繁项集挖掘算法

Algorithm for frequent item sets mining of sharing prefix based on FP-tree

数据预处理用到了聚集、抽样、离散化等方法，关联规则挖掘频繁项集主要用到了Apriori的算法。

In data processing , we use aggregation , sampling and discretization . In association analysis , we mainly use Apriori algorithm .

本文利用频繁项集的性质，来优化关联规则挖掘中的一个经典的算法Apriori；

This paper takes advantage of a character of frequent items set to improve Apriori algorithm , which is a classical algorithm of association rules .

针对Apriori算法寻找频繁项集问题，通过对事务数据库的布尔化表示，提出了一种直接利用布尔矩阵的行向量去搜寻频繁项集的思想。

An enhanced Apriori algorithm which directly used the row vectors of boolean matrix for transaction databases to find out the frequent item sets was presented in this paper .

Apriori算法是经典的频繁项集生成算法，其基本思想是用逐层搜索的迭代方法来生成频繁项集。

The Apriori algorithm is a classical algorithm that generates frequent item-sets . The basic idea of it is to use the layer-by-layer search iterative method for generating the frequent item-sets .

详细研究了关联规则数据挖掘，分析了存在的问题和不足，提出了一种频繁项集增量算法，用于对Apriori算法进行改进。

We have researched the related regular data mining , has analyzed existing problem and deficiency , propose one frequent item set increment algorithm , use for , improve to Apriori algorithm .

本文论述了关联规则的基本概念、分类、基于频繁项集思想的关联规则挖掘算法&Apriori算法，以及在基础上对Apriori算法的各种改进算法。

This paper introduces basic concepts and classification of association rules . Discussing representative algorithm of mining association rules & Apriori algorithm based on frequency item set idea and some betterment algorithms for Apriori .

在准确度大致相当的情况下，由于该方法只需要对数据集进行一次扫描，所以其时间效率明显优于以Greedy算法为代表的基于频繁项集的处理方式，大大降低了算法的复杂度。

In the case of equivalent accuracy , the efficiency of this algorithm is more superiority than the other ones , such as Greedy algorithm based on frequency items , because it need only once scan of the datasets .

3研究关联规则Apriori算法，分析了传统的关联规则理论基础、经典算法，探讨了提高Apriori算法效率的几种方法，着重介绍一种不产生候选挖掘频繁项集的方法。

In the paper we research the Apriori arithmetic of association rules , probe into several efficient methods to improve the Apriori arithmetic , and introduce emphatically a mining method without generating candidate frequent itemsets : FP-tree .

研究了增量式频繁项集挖掘算法，提出了基于FP-growth的增量式频繁项集挖掘算法，通过与Apriori算法的比较，验证了该算法的高效性。

We research on the incremental frequent item sets mining algorithms and propose a novel algorithm based on FP-growth . After being compared to the Apriori algorithm , the new algorithm was proved to be more efficient .

同时，对传统的关联技术中寻找频繁项集的Apriori算法进行改进，减少了待扫描候选项集中候选项的数量，有效地提高了寻找频繁项集的速度。

An improved algorithm originated from Apriori , which is a traditional frequency-item-seeking algorithm of association technology , is given and the improved algorithm can reduce the items to be sought thought the collection of candidate-items and accelerate the seeking speed efficiently .

基于故障信息维度表与关系规则维度表应用Apriori算法的频繁项集方法对故障信息进行分析，通过故障匹配、生成候选集、过滤候选集，最后确定故障原因，优选出排除故障方案。

The fault information is analyzed by a set of frequent items with the apriori algorithm based on the dimensionality tables of fault information and association rules . Causes of fault are found and the primary solution is chosen by fault matching , candidate generation and candidate screening .

之后，本文采用关联规则挖掘算法Apriori中的频繁项集抽取了专利知识链，并借鉴复杂网络分析中凝聚子群的识别法，以Lambda集合算法完成了专利知识群的识别与计算。

In this research , patent knowledge link has been extracted with frequency item set of Apriori mining algorithms in association rules . Taking example by identification methods of complex network analysis , patent knowledge group has been recognized and calculated with Lambda Set from social network analysis .

现有的挖掘负关联规则以及含负项目的关联规则算法为数不多，而且本质上都是基于Apriori思想的迭代算法，需要对数据集进行多次扫描，同时生成大量的候选频繁项集。

Not only the existing algorithms of mining negative association rules and association rules with negative items are very few , but also they are essentially based upon the iterative algorithms of Apriori idea , which needs multiple times scanning data sets and generating large amounts of frequent candidate sets .

GAPNAR算法首先利用Apriori算法生成频繁项集，之后利用基于相关系数的NRGA算法生成含有所有负项的关联规则，在所有规则生成后，利用遗传算法优选生成的规则。

The GA_PNAR algorithm firstly uses the Apriori algorithm to generate frequent item set , and then generates all negative association rules , through the NRGA algorithm based on correlation coefficient . When all the associations come out , genetic algorithm is adopted to optimize these rules .

为了进一步提高频繁项集挖掘算法的可扩展性，对模式树进行了细致的研究，在此基础上提出了一种挖掘频繁项集的新算法，FP-DFS算法。

To make further improvement on the scalability of the algorithm , we make a further study on the pattern tree , and propose a new algorithm called FP-DFS based on the study .

对传统集合操作进行了扩展，提出了基于扩展集合操作的最大频繁项集生成算法FIS-ES，并从理论上对算法的复杂度进行了详细的分析。

The traditional set operator has been extended firstly , the FIS-ES algorithm has been presented on the basis of extended set operator . The detailed analysis about the complexity of algorithm is done in theory and experiment .

基于频繁项集的降维在数据挖掘中的应用

Frequent item sets based dimensionality reduction algorithm in data mining research

一种基于二进制编码的频繁项集查找算法

A Algorithm of Finding Frequent Item Sets Based on Binary Coding

多维频繁项集计算方法及应用

The way to mine multidimensional frequent items and its application

一种基于多层模糊模式的频繁项集剪枝算法的优化

An Efficient Arithmetic of Pruning Frequent Set Based on Multilevel Fuzzy Mode

基于粗糙集的频繁项集挖掘算法

Mining Algorithm of Frequent Item set Based on Rough Set

FSC&利用频繁项集挖掘估算视图大小

FSC & Using Frequent Set Mining for View Size Estimation

基于多层概要结构的数据流的频繁项集发现算法

Finding Frequent Items of Data Streams Based on Hierarchical Sketch