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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 ondoi:10.1109/icde.1999.754925 dblp:conf/icde/ZakiHA99 fatcat:4ydp6hzlazhnhkainlxtfq22xe