東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Linked to FindBook

Google Book

Amazon

博客來

An introduction to web mining = with applications in R /

Record Type:	Electronic resources : Monograph/item
Title/Author:	An introduction to web mining/ by Ulrich Matter.
Reminder of title:	with applications in R /
Author:	Matter, Ulrich.
Published:	Cham :Springer Nature Switzerland : : 2025.,
Description:	xxi, 251 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	- Part I: Context, Relevance, and the Basics -- 1. Introduction -- 2. The Internet as a Data Source -- Part II: Web Technologies and Automated Data Extraction -- 3. Web 1.0 Technologies: The Static Web -- 4. Web Scraping: Data Extraction from Websites -- 5. Web 2.0 Technologies: The Programmable/Dynamic Web -- 6. Extracting Data From The Programmable Web -- 7. Data Extraction from Dynamic Websites -- Part III: Advanced Topics in Web Mining -- 8. Web Mining Programs -- 9. Crawler Implementation -- 10. Appearance and Authentication -- 11. Scaling Web Mining in the Cloud -- 12. AI Tools for Web Mining: Overview and Outlook -- Part IV: Ethical, Legal, and Scientific Rigor -- 13. Ethics and Legal Considerations -- 14. Web Mining and Scientific Rigor.
Contained By:	Springer Nature eBook
Subject:	Multimedia data mining. -
Online resource:	https://doi.org/10.1007/978-3-031-96638-5
ISBN:	9783031966385

An introduction to web mining = with applications in R /
Matter, Ulrich.

An introduction to web miningwith applications in R /[electronic resource] :by Ulrich Matter. - Cham :Springer Nature Switzerland :2025. - xxi, 251 p. :ill. (some col.), digital ;24 cm. - Use R!,2197-5744. - Use R!..

- Part I: Context, Relevance, and the Basics -- 1. Introduction -- 2. The Internet as a Data Source -- Part II: Web Technologies and Automated Data Extraction -- 3. Web 1.0 Technologies: The Static Web -- 4. Web Scraping: Data Extraction from Websites -- 5. Web 2.0 Technologies: The Programmable/Dynamic Web -- 6. Extracting Data From The Programmable Web -- 7. Data Extraction from Dynamic Websites -- Part III: Advanced Topics in Web Mining -- 8. Web Mining Programs -- 9. Crawler Implementation -- 10. Appearance and Authentication -- 11. Scaling Web Mining in the Cloud -- 12. AI Tools for Web Mining: Overview and Outlook -- Part IV: Ethical, Legal, and Scientific Rigor -- 13. Ethics and Legal Considerations -- 14. Web Mining and Scientific Rigor.

This book is devoted to the art and science of web mining - showing how the world's largest information source can be turned into structured, research-ready data. Drawing on many years of teaching graduate courses on Web Mining and on numerous large-scale research projects in web mining contexts, the author provides clear explanations of key web technologies combined with hands-on R tutorials that work in the real world - and keep working as the web evolves. Through the book, readers will learn how to - scrape static and dynamic/JavaScript-heavy websites - use web APIs for structured data extraction from web sources - build fault-tolerant crawlers and cloud-based scraping pipelines - navigate CAPTCHAs, rate limits, and authentication hurdles - integrate AI-driven tools to speed up every stage of the workflow - apply ethical, legal, and scientific guidelines to their web mining activities Part I explains why web data matters and leads the reader through a first "hello-scrape" in R while introducing HTML, HTTP, and CSS. Part II explores how the modern web works and shows, step by step, how to move from scraping static pages to collecting data from APIs and JavaScript-driven sites. Part III focuses on scaling up: building reliable crawlers, dealing with log-ins and CAPTCHAs, using cloud resources, and adding AI helpers. Part IV looks at ethical, legal, and research standards, offering checklists and case studies, enabling the reader to make responsible choices. Together, these parts give a clear path from small experiments to large-scale projects. This valuable guide is written for a wide readership - from graduate students taking their first steps in data science to seasoned researchers and analysts in economics, social science, business, and public policy. It will be a lasting reference for anyone with an interest in extracting insight from the web - whether working in academia, industry, or the public sector.

ISBN: 9783031966385

Standard No.: 10.1007/978-3-031-96638-5doiSubjects--Topical Terms:

3251475
Multimedia data mining.

LC Class. No.: QA76.9.D343

Dewey Class. No.: 006.312

An introduction to web mining = with applications in R /
LDR:03724nmm a2200337 a 4500 001 2414357
003 DE-He213
005 20250808130223.0
006 m d
007 cr nn 008maaau
008 260205s2025 sz s 0 eng d
020 $a 9783031966385 $q (electronic bk.)
020 $a 9783031966378 $q (paper)
024 7 $a 10.1007/978-3-031-96638-5 $2 doi
035 $a 978-3-031-96638-5
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.9.D343
072 7 $a PBT $2 bicssc
072 7 $a MAT029000 $2 bisacsh
072 7 $a PBT $2 thema
082 0 4 $a 006.312 $2 23
090 $a QA76.9.D343 $b M435 2025
100 1 $a Matter, Ulrich. $3 3791009
245 1 3 $a An introduction to web mining $h [electronic resource] : $b with applications in R / $c by Ulrich Matter.
260 $a Cham : $b Springer Nature Switzerland : $b Imprint: Springer, $c 2025.
300 $a xxi, 251 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Use R!, $x 2197-5744
505 0 $a - Part I: Context, Relevance, and the Basics -- 1. Introduction -- 2. The Internet as a Data Source -- Part II: Web Technologies and Automated Data Extraction -- 3. Web 1.0 Technologies: The Static Web -- 4. Web Scraping: Data Extraction from Websites -- 5. Web 2.0 Technologies: The Programmable/Dynamic Web -- 6. Extracting Data From The Programmable Web -- 7. Data Extraction from Dynamic Websites -- Part III: Advanced Topics in Web Mining -- 8. Web Mining Programs -- 9. Crawler Implementation -- 10. Appearance and Authentication -- 11. Scaling Web Mining in the Cloud -- 12. AI Tools for Web Mining: Overview and Outlook -- Part IV: Ethical, Legal, and Scientific Rigor -- 13. Ethics and Legal Considerations -- 14. Web Mining and Scientific Rigor.
520 $a This book is devoted to the art and science of web mining - showing how the world's largest information source can be turned into structured, research-ready data. Drawing on many years of teaching graduate courses on Web Mining and on numerous large-scale research projects in web mining contexts, the author provides clear explanations of key web technologies combined with hands-on R tutorials that work in the real world - and keep working as the web evolves. Through the book, readers will learn how to - scrape static and dynamic/JavaScript-heavy websites - use web APIs for structured data extraction from web sources - build fault-tolerant crawlers and cloud-based scraping pipelines - navigate CAPTCHAs, rate limits, and authentication hurdles - integrate AI-driven tools to speed up every stage of the workflow - apply ethical, legal, and scientific guidelines to their web mining activities Part I explains why web data matters and leads the reader through a first "hello-scrape" in R while introducing HTML, HTTP, and CSS. Part II explores how the modern web works and shows, step by step, how to move from scraping static pages to collecting data from APIs and JavaScript-driven sites. Part III focuses on scaling up: building reliable crawlers, dealing with log-ins and CAPTCHAs, using cloud resources, and adding AI helpers. Part IV looks at ethical, legal, and research standards, offering checklists and case studies, enabling the reader to make responsible choices. Together, these parts give a clear path from small experiments to large-scale projects. This valuable guide is written for a wide readership - from graduate students taking their first steps in data science to seasoned researchers and analysts in economics, social science, business, and public policy. It will be a lasting reference for anyone with an interest in extracting insight from the web - whether working in academia, industry, or the public sector.
650 0 $a Multimedia data mining. $3 3251475
650 0 $a R (Computer program language) $3 784593
650 1 4 $a Methodology of Data Collection and Processing. $3 3598081
650 2 4 $a Data Mining and Knowledge Discovery. $3 898250
650 2 4 $a Statistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy. $3 3538811
650 2 4 $a Statistics in Business, Management, Economics, Finance, Insurance. $3 3538572
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Use R!. $3 1306062
856 4 0 $u https://doi.org/10.1007/978-3-031-96638-5
950 $a Mathematics and Statistics (SpringerNature-11649)