基于判断聚合的分布式数据挖掘分类算法研究  

Classification Algorithm of Distributed Data Mining Based on Judgment Aggregation

作　　者：李莉 LI Li(School of Administrative law,Southwest University of Political Science and Law,Chongqing 401120,China)

机构地区：[1]西南政法大学行政法学院,重庆401120

出　　处：《计算机科学》2020年第S01期450-456,466,共8页Computer Science

基　　金：国家社科基金项目(18BZX133);西南政法大学校级项目(2016XZQN-20)。

摘　　要：随着互联网的发展和云计算技术的广泛应用,许多数据存储在不同的服务器上,分布式数据挖掘技术应运而生。智能agent在各自的站点上得到部分挖掘结果,分布式数据挖掘可以将这些部分的挖掘结果聚合成为全局的结果。文中主要处理的是分布式数据挖掘过程中的分类问题,针对一些特征的数据分别存储于不同的数据源上,提出了一种基于判断聚合模型的分类算法。该算法中每一个agent要对一个案例属于某一个目标类的可能性进行判断,然后利用判断聚合模型将这些agent的判断进行聚合,形成全局的分类结果。基于判断聚合模型的分类算法将逻辑和社会选择理论的技术应用于解决分布式数据挖掘的分类问题,这种新的算法不需要大规模地传输和转化数据,节省了传输成本,提高了分类效率,同时有效地保护了数据的安全性。With the development of Internet and the wide application of cloud computing,many data sets are stored on different servers,and the distributed data mining comes into being.Each agent gets partial data mining results on its respective site,and distributed data mining could aggregate this part of mining results into a global decision.This paper is focused on the classification issue in the process of distributed data mining.Aiming at some specific data are stored in difference data source,this paper puts forward a classification algorithm based on the Judgment aggregation model.Each agent should give its Judgment whether a new case belongs to a certain target class,and then use the Judgment aggregation model to aggregate the Judgments of these agents to form a global classification.This algorithm combines logic and social choice theory technologies together and applies them to the classification problem in distributed data mining.It doesn’t need to transfer and transform the data on a large scale,thus saving the transmission cost and improving the efficiency of classification.At the same time,it effectively protects the data security.

关 键 词：分布式数据挖掘 多主体系统 判断聚合模型 逻辑 算法 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]