完全加权正负关联规则挖掘及其在教育数据中的应用

完全加权数据模型的特点是其项目权值分布在各个事务记录中,随着事务记录的不同而变化。现有的加权负关联规则挖掘算法不能适用于完全加权数据模型。该文提出一种新颖的基于概率比和兴趣度的完全加权正负关联规则的挖掘算法,探讨了算法在教育信息化数据中的应用。算法以概率比代替传统的置信度,采用支持度-概率比-兴趣度架构衡量完全加权正负关联规则,获得很好的挖掘效果。以真实的教育数据和文本数据为实验测试集,与现有正负关联规则挖掘算法比较,该文提出的算法更有效、更合理,具有较高的理论价值和应用前景。

All-weighted Positive and Negative Association Rules Mining and Its Application in Education Data

All-weighted data model is characterized by its item weights distribution in each transaction records, changing with the different transaction records. Existing mining algorithm of weighted negative association rules can not be applied all-weighted data model. In this paper, a novel mining algorithm of all-weighted positive and negative association rules is presented for application in education data. The algorithm uses probability ratio instead of the traditional confidence, and adopts "support- probability ratio-interest" framework to estimate positive and negative all-weighted association rules. Using real educational information data and text data as test set, the algorithm proposed in this paper is more effective and more reasonable compared with the existing mining algorithms of positive and negative association rules.