摘要：

同一关联挖掘算法算法在不同性质的数据上会表现出不同的性能。针对该问题，提出一种有趣关联模式挖掘方法。介绍模式的兴趣度度量，引入兴趣度预处理过程，并将数据分为2种类型，分别采用不同的算法对这2类数据集进行挖掘。实例表明，该方法能有效提高输出模式的质量。

关键词: 关联挖掘, 水平加权支持度, 全置信度, 兴趣度, 兴趣度预处理

Abstract:

As the performance of the association mining algorithm can be changed by the property of the input data, the same algorithm can do different to the different data set. Therefore, this paper categorizes the input data into there groups, and takes different methods to deal with the first two data sets. The examples show that the above processing can improve the quantity of the output patterns. In order to develop the quantity of data mining, it simply introduces the interestingness of patterns, and the interestingness preprocessing of the association patterns. According to the above analysis, it proposes a new method of mining interesting association patterns.

Key words: association mining, horizontal weighted support, all confidence, interestingness, interestingness preprocessing association mining, horizontal weighted support, all confidence, interestingness, interestingness preprocessing

中图分类号: 

• TP301.6