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 |
Supervised Learning | 3 |
Bibliographic Notes | 12 |
341 | 53 |
Discussion | 81 |
Bibliographic Notes | 115 |
Text Documents | 138 |
20 | 139 |
Bibliographic Notes | 149 |
Web Mining | 183 |
7 | 237 |
32 | 271 |
Bibliographic Notes | 320 |
Merge Algorithm 10 9 2 Lexical Appropriateness 10 9 3 Instance Appropriateness Bibliographic Notes | 406 |
Bibliographic Notes | 482 |
31 | 145 |
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
actor anchor text applications Apriori algorithm called candidate centroids Chap class association rules class label classifier clustering algorithm compute consider contains cosine similarity crawler crawling data instances data mining data points data set decision boundary decision tree decision tree learning denoted distance endfor entropy Equation evaluation focused crawler frequent itemsets function given graph HITS algorithm hyperlinks hyperplane inverted index iteration k-means algorithm large number learning algorithm matrix method minimum support minsup MIS value mixture model multiple naïve Bayesian negative documents node outliers Own_house PageRank parameters partition Pr(c probability problem pruning PU learning query terms ranking recall relevant represent retrieval score search engines seeds sequence similarity space spam step subset supervised learning techniques text documents tion topic training data training examples unlabeled data unlabeled examples unlabeled set URLs vector Web mining words