Computer Science ›› 2020, Vol. 47 ›› Issue (6A): 450-456.doi: 10.11896/JsJkx.190700143

• Database & Big Data & Data Science • Previous Articles     Next Articles

Classification Algorithm of Distributed Data Mining Based on Judgment Aggregation

LI Li   

  1. School of Administrative law,Southwest University of Political Science and Law,Chongqing 401120,China
  • Published:2020-07-07
  • About author:LI Li, born in 1982, Ph. D, lecturer.Her research interests include modern logic and artificial intelligence.
  • Supported by:
    This work was supported by the National Social Science Foundation of China (18BZX133) and University Level ProJect of Southwest University of Political Science and Law (2016XZQN-20).

Abstract: 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.

Key words: Algorithm, Distributed data mining, Judgment aggregation model, Logic, Multi-agent system

CLC Number: 

  • TP18
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