基于改进型迭代算法的web数据关联规则挖掘  

Web data mining of association rules based on an improved iterative algorithm

作　　者：刘啸[1] 刘玉龙[2] 

机构地区：[1]江苏师范大学现代教育技术中心,徐州221116 [2]江苏师范大学计算机科学与技术学院,徐州221116

出　　处：《科技导报》2015年第3期90-94,共5页Science & Technology Review

基　　金：江苏省高校自然科学研究基金项目(11KJB450001);江苏师范大学自然科学基金项目(14XLB04)

摘　　要：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 interuet 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.

关 键 词：WEB挖掘 迭代算法 并行算法 本地计算 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论]