Abstract:

According to the existing problems of dominate cells information missing, an automatic data mining method of contingence table is purposed. According to the location of dominate cells, the categories in the original table are classified and represented by tree structure models on the basis of relevant theories of Multinomial Processing Tree(MPT) models, the following processes including hypothesis generations, parameter estimation and goodness-of-fit test is performed automatically. Application result shows the approach can effectively extract the latent interactions and peculiarity association rules.

Key words: Multinomial Processing Tree(MPT) model, contingence table, data mining, dominate cell analysis, rules extraction

摘要：

针对表格数据挖掘中优势点信息缺失的问题，提出一种列联表自动数据挖掘方法。依据原表中优势点位置，应用多项式加工树模型相关理论对原始数据进行自动树状模型拟合与聚类分组，生成待选假设关系集合，并最终完成参数估计以及拟合优度检验。通过实例证明该算法能够有效提取出优势点的隐含信息与特异规则。

关键词: 多项式加工树模型, 列联表, 数据挖掘, 优势点分析, 规则提取

CLC Number: 

• TP311.12