Apriori算法是R.Agrawal和R.Srikant于1994年提出的为布尔关联规则挖掘频繁项集的原创性质算法。正如我们将看到的，算法的名字基于这样的事实：算法使用频繁项集性质的先验性质。Apriori使用一种称作逐层搜索的迭代方法，k项集用于探索（k+1）项集。首先，通过扫描数据库，累积每个项的计数，并收集满足最小支持度的项，找出频繁1项集的集合。该集合记作L1。然后L1用于找频繁2项集的集合L2，L2用于找L3，如此下去，知道不能在找到频繁项集k项集。找每个Lk需要一次数据库全扫描。-R.Agrawal and R.Srikant Apriori algorithm is put forward in 1994 as a Boolean association rules mining frequent itemsets original nature of algorithms. As we will see, the algorithm is based on the fact the name: the nature of frequent itemsets algorithm uses a priori nature. Apriori layer by layer using a technique called iterative search method, k itemsets used to explore (k+1) itemsets. First, by scanning the database, the cumulative count of each item and collect items to meet the minimum support to find frequent a set of collections. The collection recorded as L1. Then L1 is used to find the set of frequent two sets L2, L2 is used to find L3, it goes on, you know can not find frequent itemsets k itemset. Find each Lk requires one full scan database.