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Distributed Decision‐Tree Induction in Peer‐to‐Peer Systems
Author(s) -
Bhaduri Kanishka,
Wolff Ran,
Giannella Chris,
Kargupta Hillol
Publication year - 2008
Publication title -
statistical analysis and data mining: the asa data science journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.381
H-Index - 33
eISSN - 1932-1872
pISSN - 1932-1864
DOI - 10.1002/sam.10006
Subject(s) - computer science , scalability , asynchronous communication , distributed computing , decision tree , overhead (engineering) , peer to peer , synchronization (alternating current) , tree (set theory) , data mining , computer network , database , channel (broadcasting) , mathematical analysis , mathematics , operating system
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