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- 关联规则挖掘发现大量数据中项集之间有趣的关联或相关联系，管理员要通过啊，我很努力的找的啊-Mining Association Rules found a large quantity of data between Itemsets interesting correlation or relevance, administrators should ah, I worked hard to find the ah
eclat
- A program to find frequent itemsets (also closed and maximal) with the eclat algorithm ,which carries out a depth first search on the subset lattice and determines the support of itemsets by intersecting transaction list
My_eclat
- A program to find frequent itemsets with the relim algorithm (recursive elimination), which is inspired by the FP-growth algorithm, but does its work without prefix trees or any other complicated data structures. The mai
My_relim
- A program to find frequent itemsets with the relim algorithm (recursive elimination), which is inspired by the FP-growth algorithm, but does its work without prefix trees or any other complicated data structures. The mai
apriori_improve
- 数据挖掘中频繁项集挖掘算法，改进了apriori算法，性能提高很多-Data Mining frequent itemsets mining algorithm, improved apriori algorithm, improve the performance of many
MFP-Miner
- 最大频繁项集挖掘算法。运行前需将release中的data和result数据拷贝到上一级目录下。-Maximal frequent itemsets mining algorithm. Needs to be run before the release of data and result data are copied to the directory level.
pafi-1.0.1
- 是国外相关研究人员提供的发现频繁模式（包括频繁集、频繁子图等）的最新版本算法。-Foreign researchers with the relevant frequent pattern discovery (including frequent itemsets, frequent subgraph, etc.) the latest version of algorithm.
Apriori
- Apriori算法是一种找频繁项目集的基本算法。其基本原理是逐层搜索的迭代，直到不能找到维度更高的频繁项集为止。这种方法依赖连接和剪枝这两步来实现。-Apriori algorithm is a frequent itemsets to find the basic algorithm. The basic principle is that the iterative search step by step, until a high
040410205
- 1. 可以使用任何语言来完成，例如：Java、C ++。 2. 文法采用常用的方式进行描述，例如：S→aA。 3. 以文件方式读取文法。 4. 求出项目集规范族（即所有的状态）。 5. 给出状态间的关系。 6. 给出LR(0)分析表。 7. 给定的任意符号串判定是否是文法中的句子，将分析过程用计算机打印出来 -1. You can use any language to accomplish, such as
MomentFP[1].tar
- 数据流关联规则挖掘算法moment，改算法由其作者提供。挖掘频繁闭项集。-Data Stream Association Rule Mining Algorithm moment, changed algorithm provided by the author. Mining Frequent Closed Itemsets.
lcmverfimi03btgz
- lcm2 ，挖掘最大频繁项集的好算法。关联规则挖掘。-lcm2, Mining Maximal Frequent Itemsets good algorithm. Mining Association Rules.
apriori(java)
- Apriori算法是发现关联规则领域的经典算法。该算法将发现关联规则的过程分为两个步骤：第一步通过迭代，检索出事务数据库中的所有频繁项集，即支持度不低于用户设定的阈值的项集；第二步利用频繁项集构造出满足用户最小信任度的规则-Apriori association rules algorithm is found in the field of classical algorithms. The algorithm will find t
apriori
- apriori算法的java代码，APRRORI算法使用频繁项性质的先验知识，逐层搜索迭代，用K-项集产生(K+1)-项集。APRRORI算法的一个显著特点是:利用APRIORI性质，压缩了频繁项集，提高了算法的效率。 -apriori algorithm java code, APRRORI algorithm uses the a priori nature of frequent itemsets knowledge, ste
ExFP_growth
- 可挖掘负关联规则的FP_growth算法:将负项目扩展到原始数据集，同正项目一样看成普通项目（该过程已集成到程序中），然后使用FP_growth算法挖掘含负项目的一般化频繁项集-Can be a negative association rules mining algorithm FP_growth: negative item will be extended to the original data sets,同正projects
Apriori
- 数据挖掘，找强关联规则。我和同学一起合作的，我负责频繁项集，强关联规则的稍后上传。-Data mining, finding strong association rules. I and students work together, and I am responsible for frequent itemsets and strong association rules later upload.
Apriori
- 使用逐层迭代方法基于候选产生找出频繁项集-Iterative method is based on the use of layers have to identify the candidate frequent itemsets
MinimalAssociationRulesandMiningAlgorithm
- 数据挖掘是人们从海量数据中获取有用信息的有力工 具。作为数据挖掘的重要方法之一，关联规则挖掘引起各界 人士的广泛关注。关联规则挖掘用来发现大量数据中项集之 间有趣的关联或相关关系。-Data Mining is a huge amount of data obtained from the useful information a powerful tool. Data mining as one of the import
apriori
- 用VC++實現apriori演算法，可以找尋Frequent Itemsets，用途於Data Mining是很具參考價值-With VC++ Achieve apriori algorithm can look for Frequent Itemsets, use in Data Mining is a very useful
ArithmeticofLongItemsetPreferential_ImprovedAprior
- Apriori算法是一种最有影响的挖掘布尔关联规则频繁项集的算法。本文简单介绍了Apfiofi算法，提出了Apfiofi算法的改进方案—— 长项优先的产生算法，它基于传统Apriori算法，通过改变候选项集的产生顺序来减少数据库访问。从而提高效率-Apriori algorithm is one of the most influential Boolean association rules mining frequent items
MiningAlgorithmsofN-MostFrequentItemsets
- 频繁项集挖掘算法的计算复杂性和生成的频繁项集数量随着事务集项数的增加呈指数增长，最小支持度阈值成为控制这种增长的关键．然而，实际应用中仅使用支持度阈值难以有效控制频繁项集的规模．为此定义N个 最频繁项集挖掘问题，并提出基于支持度阈值动态调整策略的宽度优先搜索算法Apriori和深度优先搜索算法IntvMatrix挖掘N个最频繁项集．实验表明，本文的2种方法的效率比朴素方法高2倍以上，特别当N值较低时，本 文方法的效率优势更为明显