Web Data Mining: Exploring Hyperlinks, Contents, and Usage DataSpringer Science & Business Media, 2007 - 532 páginas 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. |
Índice
Introduction | 1 |
1 | 8 |
Problem Definition | 32 |
Supervised Learning | 55 |
16 | 62 |
26 | 95 |
Discussion | 96 |
SingleLink Method | 133 |
Partially Supervised Learning | 151 |
Derivation of EM for Naïve Bayesian Classification | 179 |
34 | 221 |
Adaptation | 303 |
Bibliographic Notes | 410 |
Web Usage Mining | 449 |
References | 484 |
Otras ediciones - Ver todo
Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data Bing Liu Vista previa restringida - 2011 |
Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data Bing Liu No hay ninguna vista previa disponible - 2013 |
Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data Bing Liu No hay ninguna vista previa disponible - 2011 |
Términos y frases comunes
applications Apriori algorithm association rules attributes binary called centroids class label classifier clustering algorithm compute Conf contains crawler crawling d₁ Data Mining data points data records data region data set database decision boundary decision tree decision tree learning denoted discuss distance domain Equation evaluation extraction feature frequent itemsets function given graph hyperlinks hyperplane Intl inverted index iteration k-means k-means algorithm large number learning algorithm LU learning Machine Learning match matrix method minsup mixture model multiple naïve Bayesian negative documents node outliers PageRank pageview partition patterns Pr(c problem Proc produce rank relevant represent schema search engine Sect sequence sequential similarity space spam subset supervised learning tags techniques term tion topic training data training examples transaction types unlabeled data unlabeled examples unlabeled set URLs usage mining vector Web mining Web usage mining words World Wide Web