Parallel formulations of decision-tree classification algorithms

Anurag Srivastava, Eui Hong Sam Han, Vipin Kumar, Vineet Singh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

23 Scopus citations


Classification decision tree algorithms are used extensively for data mining in many domains such as retail target marketing, fraud detection, etc. Highly parallel algorithms for constructing classification decision trees are desirable for dealing with large data sets in reasonable amount of time. Algorithms for building classification decision trees have a natural concurrency, but are difficult to parallelize due to the inherent dynamic nature of the computation. We present parallel formulations of classification decision tree learning algorithm based on induction. We describe two basic parallel formulations. One is based on Synchronous Tree Construction Approach and the other is based on Partitioned Tree Construction Approach. We discuss the advantages and disadvantages of using these methods and propose a hybrid method that employs the good features of these methods. Experimental results on an IBM SP-2 demonstrate excellent speedups and scalability.

Original languageEnglish (US)
Title of host publicationProceedings - 1998 International Conference on Parallel Processing, ICPP 1998
EditorsTen H. Lai
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages8
ISBN (Electronic)0818686502
StatePublished - 1998
Event1998 International Conference on Parallel Processing, ICPP 1998 - Minneapolis, United States
Duration: Aug 10 1998Aug 14 1998

Publication series

NameProceedings of the International Conference on Parallel Processing
ISSN (Print)0190-3918


Other1998 International Conference on Parallel Processing, ICPP 1998
Country/TerritoryUnited States

Bibliographical note

Publisher Copyright:
© 1998 IEEE.


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