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130626 ||| eng |
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|a 9783540378822
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|a Liu, Bing
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| 245 |
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|a Web Data Mining
|h Elektronische Ressource
|b Exploring Hyperlinks, Contents, and Usage Data
|c by Bing Liu
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| 250 |
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|a 1st ed. 2007
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| 260 |
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|a Berlin, Heidelberg
|b Springer Berlin Heidelberg
|c 2007, 2007
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| 300 |
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|a XX, 532 p. 177 illus
|b online resource
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| 505 |
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|a Data Mining Foundations -- Association Rules and Sequential Patterns -- Supervised Learning -- Unsupervised Learning -- Partially Supervised Learning -- Web Mining -- Information Retrieval and Web Search -- Link Analysis -- Web Crawling -- Structured Data Extraction: Wrapper Generation -- Information Integration -- Opinion Mining -- Web Usage Mining
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| 653 |
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|a Statistics
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| 653 |
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|a Artificial Intelligence
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| 653 |
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|a Information Storage and Retrieval
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| 653 |
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|a Data Structures and Information Theory
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| 653 |
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|a Data mining
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| 653 |
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|a Information storage and retrieval systems
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| 653 |
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|a Information theory
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| 653 |
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|a Artificial intelligence
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| 653 |
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|a Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences
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| 653 |
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|a Data structures (Computer science)
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| 653 |
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|a Data Mining and Knowledge Discovery
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| 653 |
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|a Automated Pattern Recognition
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| 653 |
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|a Pattern recognition systems
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| 041 |
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7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b Springer
|a Springer eBooks 2005-
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| 490 |
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|a Data-Centric Systems and Applications
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| 028 |
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|a 10.1007/978-3-540-37882-2
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| 856 |
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|u https://doi.org/10.1007/978-3-540-37882-2?nosfx=y
|x Verlag
|3 Volltext
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|a 003.54
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|a 005.73
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| 520 |
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|a The rapid growth of the Web in the last decade makes it the largest p- licly accessible data source in the world. Web mining aims to discover u- ful information or knowledge from Web hyperlinks, page contents, and - age logs. Based on the primary kinds of data used in the mining process, Web mining tasks can be categorized into three main types: Web structure mining, Web content mining and Web usage mining. Web structure m- ing discovers knowledge from hyperlinks, which represent the structure of the Web. Web content mining extracts useful information/knowledge from Web page contents. Web usage mining mines user access patterns from usage logs, which record clicks made by every user. The goal of this book is to present these tasks, and their core mining - gorithms. The book is intended to be a text with a comprehensive cov- age, and yet, for each topic, sufficient details are given so that readers can gain a reasonably complete knowledge of its algorithms or techniques without referring to any external materials. Four of the chapters, structured data extraction, information integration, opinion mining, and Web usage mining, make this book unique. These topics are not covered by existing books, but yet they are essential to Web data mining. Traditional Web mining topics such as search, crawling and resource discovery, and link analysis are also covered in detail in this book
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