Web mining

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Web mining

Analyzing a website or all of the Web. Web "usage" mining determines the navigation patterns of users on a site and is derived from the server logs. Web "structure" mining examines the link hierarchy of a site in order to improve navigation. Web "content" mining explores the data contained in related sites in order to provide better resources for visitors. See data mining and text mining.
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References in periodicals archive ?
There is a wide range of Web mining, which is divided into Web usage mining, Web content mining and Web structure mining.
With respect to the main goal of analysis, web mining can be divided into three types: web content mining, web structure mining and web usage mining.
The author has organized the main body of his text in fourteen chapters devoted to social media, big data and social data, hypotheses in the era of big data, social big data applications, basic concepts in data mining, association rule mining, clustering, classification, prediction, web structure mining, web content mining, web access log mining, information extraction, and deep web mining, media mining, and scalability and outlier detection.
Web mining is divided into web content mining, web structure mining and web usage mining.
A taxonomic relationship learning approach for log ontology content event is proposed in this paper, which integrates web content mining and web usage mining.
There are three main web mining categories from the used data viewpoint: Web content mining, Web structure mining and Web usage mining (Spiliopoulou, 1999; Kosala and Blockeel, 2000; Bing, 2007).
The three-day event was held at the NYIT and JUST campuses, brought together more than 200 academics, experts, and leaders in higher education from around the world to explore issues from artificial intelligence, Web content mining and e-learning to information security, the mobile revolution, cryptography, and human-computer interaction.
The conference will also tackle artificial intelligence, mobile computing, networking, information security and cryptography, intrusion detection and computer forensics, web content mining, and other areas of interest.
Syllabus Based Web Content Extractor (SBWCE) introduces a new technique of Syllabus Based Web Content Mining. It makes the Syllabus Based Web Content Extraction easy and creates an instant online book view based on the links relevant to the given Syllabus.
With plenty of examples to guide readers from the basics to advanced techniques Markov and Larose cover the basics of web structure mining, including information retrieval and web searches and hyperlink-based ranking, web content mining, including clustering, evaluating clustering and classification and web usage mining, including preprocessing, exploratory data analysis, and modeling for web usage mining through clustering, association and classification.