Search (2 results, page 1 of 1)

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Abstract

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 slides, datasets, and implemented algorithms are available online.

Content

Inhalt: 1. Introduction 2. Association Rules and Sequential Patterns 3. Supervised Learning 4. Unsupervised Learning 5. Partially Supervised Learning 6. Information Retrieval and Web Search 7. Social Network Analysis 8. Web Crawling 9. Structured Data Extraction: Wrapper Generation 10. Information Integration

RSWK

World Wide Web / Data Mining

Subject

World Wide Web / Data Mining

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Abstract

Information retrieval (IR) is a complex human activity supported by sophisticated systems. Information science has contributed much to the design and evaluation of previous generations of IR system development and to our general understanding of how such systems should be designed and yet, due to the increasing success and diversity of IR systems, many recent textbooks concentrate on IR systems themselves and ignore the human side of searching for information. This book is the first text to provide an information science perspective on IR. Unique in its scope, the book covers the whole spectrum of information retrieval, including: history and background information; behaviour and seeking task-based information; searching and retrieval approaches to investigating information; interaction and behaviour information; representation access models; evaluation interfaces for IR; interactive techniques; web retrieval, ranking and personalization; and, recommendation, collaboration and social search multimedia: interfaces and access. A key text for senior undergraduates and masters' level students of all information and library studies courses, this book is also useful for practising LIS professionals who need to better appreciate how IR systems are designed, implemented and evaluated.

Content

Enthält die Beiträge: Interactive information retrieval: history and background / Colleen Cool and Nicholas J. Belkin - Information behavior and seeking / Peiling Wang - Task-based information searching and retrieval / Elaine G. Toms - Approaches to investigating information interaction and behaviour / Raya Fidel - Information representation / Mark D. Smucker - Access models / Edie Rasmussen - Evaluation / Kalervo Järvelin - Interfaces for information retrieval / Max Wilson - Interactive techniques / Ryen W. White - Web retrieval, ranking and personalization / Jaime Teevan and Susan Dumais - Recommendation, collaboration and social search / David M. Nichols and Michael B. Twidale - Multimedia: behaviour, interfaces and interaction / Haiming Liu, Suzanne Little and Stefan Rüger - Multimedia: information representation and access / Suzanne Little, Evan Brown and Stefan Rüger