# 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

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.