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

Authors:
Kanishka Bhaduri;Ran Wolff;Chris Giannella;Hillol Kargupta
Affiliations:
Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, Maryland, 21250, USA and Mission Critical Technologies Inc. at ...;Department of Management Information Systems, Haifa University, Haifa, 31905, Israel;Department of Computer Science, New Mexico State University, Las Cruces NM, 88003, USA;Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, Maryland, 21250, USA and Agnik, LLC., Columbia, Maryland, USA
Venue:
Statistical Analysis and Data Mining
Year:
2008

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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