Ebook: Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data

Web mining aims to discover useful information and knowledge from the Web hyperlink structure, page contents, and usage data. Although Web mining uses many conventional data mining techniques, it is not purely an application of traditional data mining due to the semistructured and unstructured nature of the Web data and its heterogeneity. It has also developed many of its own algorithms and techniques.

Liu has written a comprehensive text on Web data mining. Key topics of structure mining, content mining, and usage mining are covered both in breadth and in depth. His book brings together all the essential concepts and algorithms from related areas such as data mining, machine learning, and text processing to form an authoritative and coherent text.

The book offers a rich blend of theory and practice, addressing seminal research ideas, as well as examining the technology from a practical point of view. It is suitable for students, researchers and practitioners interested in Web mining both as a learning text and a reference book. Lecturers can readily use it for classes on data mining, Web mining, and Web search. Additional teaching materials such as lecture slid

Web mining aims to discover useful information and knowledge from the Web hyperlink structure, page contents, and usage data. Although Web mining uses many conventional data mining techniques, it is not purely an application of traditional data mining due to the semistructured and unstructured nature of the Web data and its heterogeneity. It has also developed many of its own algorithms and techniques.

Liu has written a comprehensive text on Web data mining. Key topics of structure mining, content mining, and usage mining are covered both in breadth and in depth. His book brings together all the essential concepts and algorithms from related areas such as data mining, machine learning, and text processing to form an authoritative and coherent text.

The book offers a rich blend of theory and practice, addressing seminal research ideas, as well as examining the technology from a practical point of view. It is suitable for students, researchers and practitioners interested in Web mining both as a learning text and a reference book. Lecturers can readily use it for classes on data mining, Web mining, and Web search. Additional teaching materials such as lecture slid
Content:
Front Matter....Pages I-XIX
Introduction....Pages 1-12
Association Rules and Sequential Patterns....Pages 13-54
Supervised Learning....Pages 55-116
Unsupervised Learning....Pages 117-150
Partially Supervised Learning....Pages 151-182
Information Retrieval and Web Search....Pages 183-236
Link Analysis....Pages 237-271
Web Crawling....Pages 273-321
Structured Data Extraction: Wrapper Generation....Pages 323-380
Information Integration....Pages 381-410
Opinion Mining....Pages 411-447
Web Usage Mining....Pages 449-483
Back Matter....Pages 485-532

Web mining aims to discover useful information and knowledge from the Web hyperlink structure, page contents, and usage data. Although Web mining uses many conventional data mining techniques, it is not purely an application of traditional data mining due to the semistructured and unstructured nature of the Web data and its heterogeneity. It has also developed many of its own algorithms and techniques.

Liu has written a comprehensive text on Web data mining. Key topics of structure mining, content mining, and usage mining are covered both in breadth and in depth. His book brings together all the essential concepts and algorithms from related areas such as data mining, machine learning, and text processing to form an authoritative and coherent text.

The book offers a rich blend of theory and practice, addressing seminal research ideas, as well as examining the technology from a practical point of view. It is suitable for students, researchers and practitioners interested in Web mining both as a learning text and a reference book. Lecturers can readily use it for classes on data mining, Web mining, and Web search. Additional teaching materials such as lecture slid
Content:
Front Matter....Pages I-XIX
Introduction....Pages 1-12
Association Rules and Sequential Patterns....Pages 13-54
Supervised Learning....Pages 55-116
Unsupervised Learning....Pages 117-150
Partially Supervised Learning....Pages 151-182
Information Retrieval and Web Search....Pages 183-236
Link Analysis....Pages 237-271
Web Crawling....Pages 273-321
Structured Data Extraction: Wrapper Generation....Pages 323-380
Information Integration....Pages 381-410
Opinion Mining....Pages 411-447
Web Usage Mining....Pages 449-483
Back Matter....Pages 485-532
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