一种改进的Apriori挖掘关联规则算法

关联规则挖掘可以发现大量数据中项集之间有趣的联系,并已在许多领域得到了广泛的应用。但传统关联规则挖掘很少考虑数据项的重要程度,这些算法认为每个数据对规则的重要性相同,实际挖掘的结果不是很理想。为了挖掘出更具有价值的规则,文中提出了一种加权的关联规则算法,即用频度和利润来标识该项的重要性,然后对经典Apriori算法进行改进。最后用实例对改进后算法进行验证,结果证明改进后算法是合理有效的,能够挖掘出更具价值的信息。

An Improvement of Apriori Algorithm for Mining Association Rules

Association rule mining can find interesting associations among a large set of data items,and has been applied widely in many fields.But the importance of data items is seldom considered in the traditional association rules which think every data item has the same importance for rules,actually the result of mining is not good.To explore the more valuable rules,present weighted association rule algorithms that is to use frequentness and profit to express the importance,and then improve the classical Apriori algorithms.Finally use the example to testify the improved algorithms that is reasonable and find much more valuable information.