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

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 syn...

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Veröffentlicht in:Statistical analysis and data mining Jg. 1; H. 2; S. 85 - 103
Hauptverfasser: Bhaduri, Kanishka, Wolff, Ran, Giannella, Chris, Kargupta, Hillol
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Hoboken Wiley Subscription Services, Inc., A Wiley Company 01.06.2008
Schlagworte:
data mining
decision trees
peer-to-peer
ISSN:1932-1864, 1932-1872
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Zusammenfassung: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
Bibliographie:ark:/67375/WNG-VBK0PW7B-F
ArticleID:SAM10006
istex:143C4C0F12AE36BA0CE40FC688AD5F322AC16EBF
ISSN:1932-1864
1932-1872
DOI:10.1002/sam.10006