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Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data (Data-Centric Systems and Applications) ePub download

by Bing Liu

  • Author: Bing Liu
  • ISBN: 3642072372
  • ISBN13: 978-3642072376
  • ePub: 1729 kb | FB2: 1283 kb
  • Language: English
  • Category: Computer Science
  • Publisher: Springer (November 23, 2010)
  • Pages: 552
  • Rating: 4.9/5
  • Votes: 900
  • Format: azw lrf doc lrf
Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data (Data-Centric Systems and Applications) ePub download

Although Web mining uses many conventional data mining techniques, it is not purely an application of traditional data mining due to the semi-structured and unstructured nature of the Web data. The field has also developed many of its own algorithms and techniques. Liu has written a comprehensive text on Web mining, which consists of two parts.

Data-Centric Systems and Applications. Although the book is entitled Web Data Mining, it also includes the main topics of data mining and information retrieval since Web mining uses their algorithms and techniques extensively. The data mining part mainly consists of chapters on association rules and sequential patterns, supervised learning (or classification), and unsupervised learning (or clus-tering), which are the three fundamental data mining tasks.

Although Web mining uses many conventional data mining techniques, it is not purely an. .Bing Liu. Series Title. Data-Centric Systems and Applications.

Although Web mining uses many conventional data mining techniques, it is not purely an application of traditional data mining due to the semi-structured and unstructured nature of the Web data. This is a textbook about data mining and its application to the Web. Liu succeeds in helping readers appreciate the key role that data mining and machine learning play in Web applications.

Data mining of massive data sets is transforming the way we think about crisis response, marketing. open source nature, simplicity, applicability to data analysis, and the abundance of libraries for any. Data Visualization and Exploration with R A Practical Guide to Using R RStudio and Tidyverse for Data Visualization Exploration and Data Science Applications. 77 MB·19,438 Downloads·New! open source nature, simplicity, applicability to data analysis, and the abundance of libraries for any. Improving Measurement of Productivity in Higher Education. 78 MB·5,038 Downloads·New! "Committee on National Statistics, Board on Testing and Assessment, Division.

Freytag G. Gardarin W. Jonker V. Krishnamurthy . A. Neimat P. Valduriez G. Weikum . Y. 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.

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.

Bing Liu. This book provides a comprehensive text on Web data mining. ISBN 13: 9783540378815. Series: Data-Centric Systems and Applications. File: PDF, . 6 MB. Читать онлайн. Key topics of structure mining, content mining, and usage mining are covered. The 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.

Although Web mining uses many conventional data mining techniques, it is not purely an application of traditional data mining due to the semi-structured and unstructured . Published in. Data-Centric Systems an. 006. The first part covers the data mining and machine learning foundations, where all the essential concepts and algorithms of data mining and machine learning are presented.

Web Data Mining book. Goodreads helps you keep track of books you want to read.

This book provides a comprehensive text on Web data mining. Key topics of structure mining, content mining, and usage mining are covered. The 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. It is suitable for students, researchers and practitioners interested in Web mining. Lecturers can readily use it for classes on data mining, Web mining, and Web search. Internet support with lecture slides and project problems is available online.
Xanna
What I liked most about the book was the scratch I got when facing all the possibilities regarding data that is free available on the Internet. My interest area is crawling, and there is an exclusive chapter about it on the book. But as with all others chapters, it's only a bird's-eye view on the topic, so specifically the crawler part of the book wasn't of much use. In spite of it, my expectations were reached with the rest of the work, since I just wanted to be aware of what is happening today concerning Web data mining. I must note that, although chapters on relevant topics are small (more or less 30 and so pages) and surely don't cover all the nuances, the book comes with plenty of references for anyone who wants to dig further.
Blackworm
This is a very well-written book. It is also a highly ambitious project as the book covers breadth and depth of many web analytics and data mining/machine learning related topics. It is also written in a very accessible way but still delivers strong technical knowledge for technical audience. I don't think this book has much competition in this area (so far), and the author clearly is a real expert.
Saberdragon
I'm taking Bing Liu's Data Mining course at the moment (Spring 2017), and I love this book. Didn't plan to get this book at first, but as the content of this book covers vastly and deeply most topics of Data Mining, I was not hesitate to get one.
sunrise bird
This book makes a great text for graduate courses, as well as a reference for scholars. The chapters are well written and provide good examples for any significant concepts. Each section covers the basics to establish a foundation of understanding for someone unfamiliar with the area, but goes on to also touch upon the research forefront on each topic. One of the most useful sections I've found as a researcher is the Bibliographic Notes found at the end of each section which briefly describes the major groups of work within the topic with cites to major papers/articles/books in each of these areas (seems to be about 50 or so per chapter).

The only "drawback" to this book would be if you wanted to touch upon everything, there is far too much content for a single semester. However as mentioned above, the chapters are structured such that you could easily use the first couple sections of each chapter to cover all the foundations and either leave later sections for students to read on their own/select an advanced project, or cover the remainder in a 2nd semester.

I highly recommend this book to any graduate looking for a comprehensive text and reference on web mining.

(In the interest of full disclosure, I am listed in the acknowledgements from providing feedback on a pre-print edition of the text that was used as our course textbook. I do not get royalties from sales in any way.)
Rgia
This book is very practical and well written. I learned a lot after reading this book.
Eigonn
Clearly explained a LOT of algorithms concisely in this volume. Better than all other books in the field in my opinion.
Hono
So what does the author, Bing Liu know about Web data mining to write the book "Web Data Mining - Exploring Hyperlinks, Contents, and Usage Data"[1] ? Fortunately the answer is "a lot!" This fact along with the title which had some cosine similarity with the names of my research lab and a graduate course that I have been teaching at the University of Louisville since 2004, and prior to that at the University of Memphis since 2000, are the reasons why I ordered a copy of this book. Bing Liu is a well seasoned researcher who has made significant contributions to association rule mining, in particular classification using association rule mining and association rule mining with multiple supports. He has also worked on Web data extraction, and more recently on opinion mining. In addition to the expertise of the author, two of the chapters, Chapter 8, Web Crawling, and Chapter 12, Web Usage Mining, were contributed by two leading experts in these respective areas, Filippo Menczer for the former and Bamshad Mobasher for the latter.
This book is appropriate for students at the graduate or senior undergraduate level, for practitioners in industry, and even as a good comprehensive reference for researchers in academia.
The Table of Contents held a surprise for someone who had always found it hard to limit the number of textbooks to one book in a web mining course that does not have data mining as prerequisite, and thus typically prescribes a good data mining book to introduce data mining techniques, in addition to a second book related to web mining. This book, on the other hand, has two parts, one devoted to data mining, and the other devoted to Web mining. While it was not a problem to find a very good data mining book (I have a few of them on my bookshelf), it was harder to find a book that addressed data mining and Web mining. It was also hard to find a good and comprehensive Web mining book, since most of them tend to focus on one or only two of the three main Web mining areas of Web structure, content, and usage mining (typically leaving Web usage mining in the dark, with just a small section, citing that it is an emerging area). This book, on the other hand, is a serious book on Web mining that also devotes a decent portion to data mining. I would describe the way the topics are presented as deep and rigorous enough in most chapters, which is in contrast to a large number of books on data mining and web mining. That said, because the book is full of simple examples that illustrate the methods being discussed, it is useful even for beginners, making it also appropriate for an introductory level course.
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