Parallel classification for data mining on shared-memory multiprocessors

M.J. Zaki, Ching-Tien Ho, R. Agrawal
<span title="">1999</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="" style="color: black;">Proceedings 15th International Conference on Data Engineering (Cat. No.99CB36337)</a> </i> &nbsp;
We present parallel algorithms for building decision-tree classi ers on shared-memory multiprocessor SMP systems. The proposed algorithms span the gamut of data and task parallelism. The data parallelism is based on attribute scheduling among processors. This basic scheme is extended with task pipelining and dynamic load balancing to yield faster implementations. The task parallel approach uses dynamic subtree partitioning among processors. We e v aluate the performance of these algorithms on
more &raquo; ... o machine con gurations: one in which data is too large to t in memory and must be paged from a local disk as needed and the other in which memory is su ciently large to cache the whole data. This performance evaluation shows that the construction of a decision-tree classi er can be e ectively parallelized on an SMP machine with good speedup. For the local disk con guration, the speedup ranged from 2.97 to 3.86 for the build phase and from 2.20 to 3.67 for the total time on a 4-processor SMP. F or the large memory con guration, the range of speedup was from 5.36 to 6.67 for the build phase and from 3.07 to 5.98 for the total time on an 8-processor SMP.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="">doi:10.1109/icde.1999.754925</a> <a target="_blank" rel="external noopener" href="">dblp:conf/icde/ZakiHA99</a> <a target="_blank" rel="external noopener" href="">fatcat:4ydp6hzlazhnhkainlxtfq22xe</a> </span>
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