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Description: 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 higher dimension can not find frequent itemsets ending. This method relies on a connection and pruning to achieve these two steps.
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Description: 数据挖掘（概念与技术）书上的apiori算法实现。apiori剪枝算法使用候选项集产生发现频繁项集。-Data Mining (conceptual and technical) in a book apiori algorithm. apiori pruning algorithm uses candidate item set generated frequent itemsets found.
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Author: nash |
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Description: Apriori算法是一个关联规则挖掘算法，它开创性地使用给予支持度的剪枝技术，系统的控制选项集指数增长-Apriori algorithm is an algorithm for mining association rules, which pioneered the use of pruning techniques to support the system of control options set the exponential growth
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Author: linxian |
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Description: Apriori核心算法过程如下：
过单趟扫描数据库D计算出各个1项集的支持度，得到频繁1项集的集合。
连接步：为了生成，预先生成,由2个只有一个项不同的属于的频集做一个（k-2）JOIN运算得到的。
剪枝步：由于是的超集，所以可能有些元素不是频繁的。在潜在k项集的某个子集不是中的成员是，则该潜在频繁项集不可能是频繁的可以从中移去。
通过单趟扫描数据库D，计算中各个项集的支持度，将中不满足支持度的项集去掉形成。-Apriori core algorithm process is as follows:
After single trip brain-scan database D calculated each 1 itemsets support and get frequent 1 itemsets collection.
Connect step: in order to create, a predetermined by 2 generate, only one item different belongs to the frequency of the set a (k- 2) JOIN operation get.
The pruning step: because yes superset and may, therefore, some elements are not frequent. In potential k itemsets is a subset of the members of the potential is, the frequent itemsets impossible is the frequent can find removed.
Through a single trip brain-scan database D, calculation of various itemsets support and will not satisfy the support degree itemsets removed form.
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Author: lixiongxi |
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Description: 一种改进的aprior算法，用二进制的编码计算来取代传统算法的剪枝和联接操作，可以避免多次扫描系统和减少候选集，提高运算效率-An improved aprior algorithm, using binary coding calculations to replace traditional methods of pruning and join operations, to avoid repeated scanning system and reduce the candidate sets, improve operational efficiency
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Size: 186368 |
Author: 徐伟清 |
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