基于相关集合的数据挖掘理论基础研究

1 引言到目前为止,人们已经发现了很多数据挖掘方法,如Apriori算法、决策树方法、贝叶斯(Bayes)方法、人工神经网络方法,等等;各式各样的数据挖掘理论被提出与采用,如模糊集合、粗糙集(Rough sets)理论、数理统计、机器学习、人工神经网络、决策树、模式识别、高性能计算等;各种各样的数据被挖掘,从传统的关系数据库到各种文本数据、空间数

A Study on Data Mining Theoretical Frameworks Based on Correlativity Sets

The plausibility relation which is generalization of fuzzy relation and probabilistic relation is proposed in the paper. We think data mining to be a process of finding the plausibility relation in database and correlativity measure to be a particular plausibility relation based on correlativity sets. The critical calculates such as the accuracy of the rough sets, the confidence and the bayesian form in data mining can be united using the correlativity measure. The GPDM (General Process of Data Mining)that represents the nature of data mining is also proposed. The data mining theoretical foundation and frameworks based on correlativity sets are also given and discussed in the paper.