发现含有第一类项目约束的频繁集的快速算法

FAST ALGORITHMS FOR FINDING FIRST-CLASS FREQUENT ITEMSETS WITH ITEM CONSTRAINTS

与 Apriori- like类型的算法相比 ,Zaki提出的基于垂直数据库结构及基于网格理论的算法将关联规则挖掘的运行速度提高了一个数量级 ,并且这些算法非常适合挖掘低支持度、长模式的关联规则 .以 Ecalt算法为原型 ,讨论了如何将项目约束引入关联规则挖掘过程的问题 ,从理论上证明了引入约束后的 Eclat+算法可以大大提高算法的效率和速度 ,并对相关的算法进行了比较 .

Compared with a priori-like algorithms, algorithms based on vertical data structure and lattice theory presented by Zaki can improve the running speed with an order of magnitude, and these algorithms are very suitable for mining low support and long patterns. The Eclat algorithm is used as the prototype for discussion of the problem of association rule mining with item constraints. It is proved in theory that with the introduction of item constraints, Eclat+ could greatly improve the efficiency and speed of association rule mining, and the Eclat+ is compared with some other related algorithms.