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 language||English (US)|
|Title of host publication||Proceedings - 1998 International Conference on Parallel Processing, ICPP 1998|
|Editors||Ten H. Lai|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||8|
|State||Published - 1998|
|Event||1998 International Conference on Parallel Processing, ICPP 1998 - Minneapolis, United States|
Duration: Aug 10 1998 → Aug 14 1998
|Name||Proceedings of the International Conference on Parallel Processing|
|Other||1998 International Conference on Parallel Processing, ICPP 1998|
|Period||8/10/98 → 8/14/98|
Bibliographical noteFunding Information:
This work was supported by NSF grant ASC-9634719, Army Research Office contract DA/DAAH04-95-1-0538, Cray Research Inc.
© 1998 IEEE.