面向Web挖掘的数据挖掘技术综述

Mehdi Gheisari, Hooman Hamidpour, Yang Liu, Peyman Saedi, Arif Raza, Ahmad Jalili, Hamidreza Rokhsati, Rashid Amin
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引用次数: 1

摘要

数据挖掘(DM)是由搜索、提取和分析大型数据集中的模式组成的计算过程,包括人工智能、机器学习、统计学和数据库方案交叉的方法。具体来说,它的主要目标是从原始数据集中提取信息,并将其转换为预期的结构以供进一步使用。web挖掘(web mining, WM)是数据决策的一个发展方向,它指的是数据决策的整体和相关的例程。它用于自动从web记录和服务中发现和提取信息,也就是说,WM的目的是从万维网中获取有价值的数据。鉴于其重要性,本文有必要对WM中的DM技术进行研究。
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Data Mining Techniques for Web Mining: A Survey
The data mining (DM) is the computational process that consists of searching, extracting, and analyzing patterns in large data sets, including methods at the intersection of artificial intelligence, machine learning, statistics, and database schemes. Specifically, its primary goal is to extract information from a raw data set and transform it into an expected structure for further use. Moreover, an evolving perspective of DM is web mining (WM), which refers to the whole of DM and related routines. It is used to discover and extract information from web records and services automatically, that is, WM’s purpose is to obtain valuable data from the World Wide Web. Due to its importance, a survey about DM techniques in WM is necessary, as performed in this paper.
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