A game theoretic approach to active distributed data mining

Xiaofeng Zhang*, Kwok Wai CHEUNG

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

3 Citations (Scopus)

Abstract

Learning-from-abstraction(LFA) is a recently proposed model-based distributed data mining approach which aims to the mining process both scalable and privacy preserving. However how to set the right trade-off between the abstraction levels of the local data sources and the global model, accuracy is crucial for getting the optimal abstraction, especially when the local data are inter-correlated to different extents. In this paper, we define the optimal abstraction task as a game and compute the Nash equilibrium as its solution. Also, we propose an iterative version of the game so that the Nash equilibrium can be computed by actively exploring details from the local sources in a need-to-know manner. We tested the proposed game theoretic approach using a number of data sets for model-based clustering with promising results obtained.

Original languageEnglish
Title of host publicationProceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology, IAT 2007
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages109-115
Number of pages7
ISBN (Print)0769530273, 9780769530277
DOIs
Publication statusPublished - 2007
EventIEEE Computer Society Technical Committee on Intelligent Informatics(TCII) - Fremont, CA, United States
Duration: 2 Nov 20075 Nov 2007

Publication series

NameProceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology, IAT 2007

Conference

ConferenceIEEE Computer Society Technical Committee on Intelligent Informatics(TCII)
Country/TerritoryUnited States
CityFremont, CA
Period2/11/075/11/07

Scopus Subject Areas

  • Computer Networks and Communications
  • Software

User-Defined Keywords

  • Active learning
  • Distributed data mining
  • Game theory
  • Privacy preservation

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