Get access

Distributed Decision-Tree Induction in Peer-to-Peer Systems

Authors

  • Kanishka Bhaduri,

    Corresponding author
    1. Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, Maryland, 21250, USA
    2. Mission Critical Technologies Inc. at NASA Ames Research Center, Moffett Field, CA 94035
    • Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, Maryland, 21250, USA
    Search for more papers by this author
  • Ran Wolff,

    1. Department of Management Information Systems, Haifa University, Haifa, 31905, Israel
    Search for more papers by this author
  • Chris Giannella,

    1. Department of Computer Science, New Mexico State University, Las Cruces NM, 88003, USA
    Search for more papers by this author
  • Hillol Kargupta

    1. Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, Maryland, 21250, USA
    2. Agnik, LLC., Columbia, Maryland, USA
    Search for more papers by this author

Abstract

This paper offers a scalable and robust distributed algorithm for decision-tree induction in large peer-to-peer (P2P) environments. Computing a decision tree in such large distributed systems using standard centralized algorithms can be very communication-expensive and impractical because of the synchronization requirements. The problem becomes even more challenging in the distributed stream monitoring scenario where the decision tree needs to be updated in response to changes in the data distribution. This paper presents an alternate solution that works in a completely asynchronous manner in distributed environments and offers low communication overhead, a necessity for scalability. It also seamlessly handles changes in data and peer failures. The paper presents extensive experimental results to corroborate the theoretical claims. Copyright © 2008 Wiley Periodicals, Inc., A Wiley Company Statistical Analy Data Mining 1: 000-000, 2008

Get access to the full text of this article

Ancillary