Document Type

Conference Paper

Department/Unit

Department of Computer Science

Title

A game theoretic approach to active distributed data mining

Language

English

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.

Keywords

Game theory, Data mining, Data privacy, Nash equilibrium, Protection, Distributed decision making, Intelligent agent, Computer science, Testing, Cost function, active learning, Distributed data mining, privacy preservation, game theory

Publication Date

11-2007

Source Publication Title

Proceedings of the 2007 IEEE/WIC/ACM International Conference on Intelligent Agent Technology

Editors

Lin, Tsau Young ; Bradshaw, Jeffrey M. ; Klusch, Matthias ; Zhang, Chengqi ; Broder, Andrei ; Ho, Howard

Start Page

109

End Page

115

Conference Location

Fremont, United States

Publisher

IEEE

Peer Reviewed

1

DOI

10.1109/IAT.2007.82

Link to Publisher's Edition

http://dx.doi.org/10.1109/IAT.2007.82

ISBN (print)

9780769530277

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