摘要： 分析了数据挖掘领域面临的性能问题(主要包括算法的有效性、可伸缩性和并行性);根据数据并行的思想,提出了在时序预测中并行训练神经网络的模型,以提高训练速度。这一模型具有良好的可扩展性,能适应大训练集的情况,是一种粗粒度的并行,且易于在集群系统这样的并行环境下进行数据挖掘。同时,描述了相关算法,并对训练速度进行了测试。

关键词: 数据挖掘, 数据预测, 并行计算, 神经网络

Abstract: The paper analyzes the requirement of performance, mainly including validity, flexibility and parallelism of algorithm in data mining. Based on the idea of data parallelism, a parallel training model for RBF (radial basis function) neural network in time-series prediction to improve the training speed is proposed. This model has good expansibility can cope with large training set, and is a kind of coarse granular parallel and suitable to data mining on parallel system such as cluster. Also the algorithm is described and the training speed is tested.

中图分类号: 

• TP183