Article(id=1242133082472522330, tenantId=1146029695717560320, journalId=1146031591421210625, issueId=1242133055423455754, articleNumber=null, orderNo=17, doi=10.3981/j.issn.1000-7857.2015.03.015, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1407859200000, receivedDateStr=2014-08-13, revisedDate=1413129600000, revisedDateStr=2014-10-13, acceptedDate=null, acceptedDateStr=null, onlineDate=1425368159913, onlineDateStr=2015-03-03, pubDate=1423756800000, pubDateStr=2015-02-13, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1425368159913, onlineIssueDateStr=2015-03-03, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1774077204573, creator=sys-migrate, updateTime=1774077204573, updator=sys-migrate, issue=Issue{id=1242133055423455754, tenantId=1146029695717560320, journalId=1146031591421210625, year='2015', volume='33', issue='3', pageStart='1', pageEnd='128', issueExtLink='null', onlineDate='null', pubDate='1423756800000', pubDateStr='2015-02-13', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=3, issueType=-1, specialIssue=null, createTime=1774077198126, creator='sys-migrate', updateTime=1774077198126, updator='sys-migrate', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=90, endPage=94, ext={EN=ArticleExt(id=1242133085840548462, articleId=1242133082472522330, tenantId=1146029695717560320, journalId=1146031591421210625, language=EN, title=Web data mining of association rules based on an improved iterative algorithm, columnId=1242116080374710456, journalTitle=Science & Technology Review, columnName=Articles, runingTitle=null, highlight=null, articleAbstract=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., authors=LIU Xiao1 , LIU Yulong2 , authorsList=LIU Xiao, LIU Yulong, authorCompany=1. Modern Education Technology Center, Jiangsu Normal University, Xuzhou 221116, China;1 , 刘玉龙2 , authorsList=刘啸, 刘玉龙, authorCompany=1. 江苏师范大学现代教育技术中心, 徐州221116;
科技导报
| 研究论文 2015, 33(3): 90-94
基于改进型迭代算法的web数据关联规则挖掘
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刘啸1 , 刘玉龙2
作者信息
1. 江苏师范大学现代教育技术中心, 徐州221116;
通讯作者:
刘玉龙,教授,研究方向为离散数学与算法分析、信息安全,电子信箱:ylliu@163.com
Web data mining of association rules based on an improved iterative algorithm
Affiliations
出版时间: 2015-02-13
doi: 10.3981/j.issn.1000-7857.2015.03.015
文章导航
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。
web 挖掘
/
迭代算法
/
并行算法
/
本地计算
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.
web mining
/
iterative algorithm
/
parallel algorithmic
/
local computing
刘啸, 刘玉龙.
基于改进型迭代算法的web数据关联规则挖掘.
科技导报,
2015
, 33
(3)
: 90
-94
.
DOI: 10.3981/j.issn.1000-7857.2015.03.015
LIU Xiao, LIU Yulong.
Web data mining of association rules based on an improved iterative algorithm[J].
Science & Technology Review ,
2015
, 33
(3)
: 90
-94
.
DOI: 10.3981/j.issn.1000-7857.2015.03.015
2015年第33卷第3期
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文章信息
doi: 10.3981/j.issn.1000-7857.2015.03.015
接收时间：2014-08-13
首发时间：2015-03-03
出版时间：2015-02-13
收稿日期：2014-08-13
修回日期：2014-10-13
通讯作者:
刘玉龙,教授,研究方向为离散数学与算法分析、信息安全,电子信箱:ylliu@163.com
https://castjournals.cast.org.cn/joweb/kjdb/CN/10.3981/j.issn.1000-7857.2015.03.015
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2种不同金属材料的力学参数
科 属数 种数 占总种数比例 属 种数 占总种数比例 鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78 小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39 多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39 红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87 小菇属 Mycena 11 5.26 光柄菇属 Pluteus 5 2.39 红菇属 Russula 17 8.13 栓菌属 Trametes 5 2.39
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