The Big Data Mining Approach for Finding top rated URL

J. S. Shyam Mohan, P. Shanmugapriya, Bhamidipati Vinay Pawan Kumar
{"title":"The Big Data Mining Approach for Finding top rated URL","authors":"J. S. Shyam Mohan, P. Shanmugapriya, Bhamidipati Vinay Pawan Kumar","doi":"10.1515/jacsm-2015-0007","DOIUrl":null,"url":null,"abstract":"Abstract Finding out the widely used URL’s from online shopping sites for any particular category is a difficult task as there are many heterogeneous and multi-dimensional data set which depends on various factors. Traditional data mining methods are limited to homogenous data source, so they fail to sufficiently consider the characteristics of heterogeneous data. This paper presents a consistent Big Data mining search which performs analytics on text data to find the top rated URL’s. Though many heuristic search methods are available, our proposed method solves the problem of searching compared with traditional methods in data mining. The sample results are obtained in optimal time and are compared with other methods which is effective and efficient.","PeriodicalId":89404,"journal":{"name":"Journal of Applied Computer Science Methods","volume":"215 1","pages":"17 - 32"},"PeriodicalIF":0.0000,"publicationDate":"2015-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/jacsm-2015-0007","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Applied Computer Science Methods","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1515/jacsm-2015-0007","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Abstract Finding out the widely used URL’s from online shopping sites for any particular category is a difficult task as there are many heterogeneous and multi-dimensional data set which depends on various factors. Traditional data mining methods are limited to homogenous data source, so they fail to sufficiently consider the characteristics of heterogeneous data. This paper presents a consistent Big Data mining search which performs analytics on text data to find the top rated URL’s. Though many heuristic search methods are available, our proposed method solves the problem of searching compared with traditional methods in data mining. The sample results are obtained in optimal time and are compared with other methods which is effective and efficient.
寻找顶级URL的大数据挖掘方法
摘要由于网络购物网站中存在许多异构、多维的数据集,而且这些数据集受各种因素的影响,因此从网站中找出广泛使用的某一特定类别的URL是一项艰巨的任务。传统的数据挖掘方法仅限于同质数据源,未能充分考虑异构数据的特点。本文提出了一种一致的大数据挖掘搜索,它对文本数据进行分析,以找到排名靠前的URL。虽然有许多启发式搜索方法,但我们提出的方法解决了数据挖掘中传统方法无法解决的搜索问题。在最优时间内得到了样品结果,并与其他方法进行了比较,结果是有效的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信
小红书