Mining the Web : Discovering Knowledge From Hypertext Data

I still gave it 5 stars though the effective page number is 326. There are mainly 3 sections in the book --- the first section is 79 pages walks you thru the basic structure of a web search engine, the 2nd one talks about the learning process (clustering, classification and so on), yes, I know it is AI related stuffs, but this book does not have too much equation and is quite readable. From page 203 is the 3rd section --- application which includes page ranking and other interesting topics. Mining the Web: Discovering Knowledge from Hypertext Data is the first book devoted entirely to techniques for producing knowledge from the vast body of unstructured Web data. Building on an initial survey of infrastructural issues—including Web crawling and indexing—Chakrabarti examines low-level machine learning techniques as they relate specifically to the challenges of Web mining. He then devotes the final part of the book to applications that unite infrastructure and analysis to bring machine learning to bear on systematically acquired and stored data. Here the focus is on results: the strengths and weaknesses of these applications, along with their potential as foundations for further progress. From Chakrabarti's work—painstaking, critical, and forward-looking—readers will gain the theoretical and practical understanding they need to contribute to the Web mining effort.

* A comprehensive, critical exploration of statistics-based attempts to make sense of Web Mining.
* Details the special challenges associated with analyzing unstructured and semi-structured data.
* Looks at how classical Information Retrieval techniques have been modified for use with Web data.
* Focuses on today's dominant learning methods: clustering and classification, hyperlink analysis, and supervised and semi-supervised learning.
* Analyzes current applications for resource discovery and social network analysis.
* An excellent way to introduce students to especially vital applications of data mining and machine learning technology. Mining.the.Web......Page 1 FOREWORD......Page 7 PREFACE......Page 16 chapter 1 INTRODUCTION ......Page 20 part i INFRASTRUCTURE......Page 34 chapter 2 CRAWLING THE WEB......Page 36 chapter 3 WEB SEARCH AND INFORMATION RET RIEVAL......Page 64 part ii LEARNING......Page 96 chapter 4 SIMILARITY AND CLUSTERING ......Page 98 chapter 5 SUPERVISED LEARNING ......Page 144 chapter 6 SEMISUPERVISED LEARNING ......Page 196 part iii APPLICATIONS ......Page 220 chapter 7 SOCIAL NETWORK ANALYSIS ......Page 222 chapter 8 RESOURCE DISCOVERY......Page 274 chapter 9 THE FUTURE OF WEB MINING ......Page 308 REFERENCES......Page 326 I N D E X......Page 345 Examines low-level machine learning techniques as they relate specifically to the challenges of Web mining. This work focuses on applications that unite infrastructure and analysis to bring machine learning to bear on systematically acquired and stored data. The World Wide Web is the largest and most widely known repository of hypertext.