Parallel Classification for Data Mining on Shared-Memory Multiprocessors

We present parallel algorithms for building decision-tree classifiers 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. Our performance evaluation shows that the construction of a decision-tree classifier can be effectively parallelized on an SMP machine with good speedup.

• Type: Conference
• Year: 1999
• Where: ICDE
• Authors: Mohammed Javeed Zaki, Ching-Tien Ho, Rakesh Agrawal
• Keywords: Database, ICDE 1999, Task Parallel Approach, Task Parallelism, Task Pipelining