稠密数据库有趣规则的快速挖掘

目前，关联规则的挖掘算法主要用于非稠密数据，很少有挖掘稠密数据的算法，而现实世界中有许多数据是稠密的。由于现行的算法不适合稠 密数据的挖掘，本文定义了兴趣度函数，给出了挖掘稠密数据有趣关联的有效算法。该算法与以往不同的是提前过滤掉不满足约束的属性组合，因而提高了速度，同时，使发现的规则更有趣，更易理解。

Intersting Rules Fast Mining in Dense Databases

There are many algorithms used for data sets which are not dense in the current researches of association rules, but few for dense data sets. However, many data sets tend to be dense in the real world. Because the current algorithms are ineffective for dense data, the interesting function is defined and an effective algorithm that discovers the interesting rules is presented in this paper. The algorithm is different from the currents. It discards the combinations of items that do not meet constraints as soon as possible, so this method is more effective. On the other hand , it makes discovered rules more interesting and more easily understood.