因特网上的数据越来越多、越来越复杂,这些异构、动态、分布的信息使得传统数据挖掘方式已经不能达到实际要求。本文提出了一种面向web 数据挖掘的改进型迭代算法,将迭代方法与多服务器并行算法进行结合,并采用该算法建立了一个支持并行关联规则的web 数据挖掘模型,融合存储节点本地计算的思想。实验证明,该模型能够提高web 数据挖掘的效率,并有随着数据量增加执行率升高的特点。
With the increasing dependency of all aspects of social life on Internet, the data on the internet is becoming more and more massive, and also more complex. This heterogeneous and dynamic information which is also distributed makes the traditional data mining unable to achieve actual requirements. This paper proposes an improved iterative algorithm for web data mining: combining iteration method with a parallel algorithm. And a web data mining mode is set up by the algorithm with the idea of local computing of storage nodes, which supports the parallel association rule. Experimental results show that this mode can improve the efficiency of web data mining and its implementation rate will rise as the data quantity increases.
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