LEADER 05348nam 22007215 450 001 9911020417803321 005 20260211155322.0 010 $a9783031966385$b(electronic bk.) 024 7 $a10.1007/978-3-031-96638-5 035 $a(MiAaPQ)EBC32256968 035 $a(Au-PeEL)EBL32256968 035 $a(CKB)40150642400041 035 $a(DE-He213)978-3-031-96638-5 035 $a(EXLCZ)9940150642400041 100 $a20250808h20252025 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 13$aAn introduction to web mining $ewith applications in R /$fUlrich Matter 210 1$aCham :$cSpringer,$d[2025] 210 4$d©2025 215 $a1 online resource (xxi, 251 pages) $cillustrations 225 1 $aUse R!,$x2197-5744 311 08$aPrint version: Matter, Ulrich An Introduction to Web Mining Cham : Springer,c2025 9783031966378 320 $aIncludes bibliographical references and index. 327 $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. 330 $aThis book is devoted to the art and science of web mining ? showing how the world's largest information source can be turned into structured, research-ready data. Drawing on many years of teaching graduate courses on Web Mining and on numerous large-scale research projects in web mining contexts, the author provides clear explanations of key web technologies combined with hands-on R tutorials that work in the real world ? and keep working as the web evolves. Through the book, readers will learn how to - scrape static and dynamic/JavaScript-heavy websites - use web APIs for structured data extraction from web sources - build fault-tolerant crawlers and cloud-based scraping pipelines - navigate CAPTCHAs, rate limits, and authentication hurdles - integrate AI-driven tools to speed up every stage of the workflow - apply ethical, legal, and scientific guidelines to their web mining activities Part I explains why web data matters and leads the reader through a first ?hello-scrape? in R while introducing HTML, HTTP, and CSS. Part II explores how the modern web works and shows, step by step, how to move from scraping static pages to collecting data from APIs and JavaScript-driven sites. Part III focuses on scaling up: building reliable crawlers, dealing with log-ins and CAPTCHAs, using cloud resources, and adding AI helpers. Part IV looks at ethical, legal, and research standards, offering checklists and case studies, enabling the reader to make responsible choices. Together, these parts give a clear path from small experiments to large-scale projects. This valuable guide is written for a wide readership ? from graduate students taking their first steps in data science to seasoned researchers and analysts in economics, social science, business, and public policy. It will be a lasting reference for anyone with an interest in extracting insight from the web ? whether working in academia, industry, or the public sector. 410 0$aUse R!,$x2197-5744 606 $aMultimedia data mining 606 $aR (Computer program language) 606 $aSocial sciences$xStatistical methods 606 $aStatistics 606 $aMethodology of Data Collection and Processing 606 $aData Mining and Knowledge Discovery 606 $aStatistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy 606 $aStatistics in Business, Management, Economics, Finance, Insurance 606 $aMineria de dades$2thub 606 $aR (Llenguatge de programació)$2thub 606 $aEstadística$2thub 606 $aMetodologia de les cičncies socials$2thub 608 $aLlibres electrňnics$2thub 615 0$aMultimedia data mining 615 0$aR (Computer program language) 615 0$aSocial sciences$xStatistical methods. 615 0$aStatistics. 615 14$aMethodology of Data Collection and Processing. 615 24$aData Mining and Knowledge Discovery. 615 24$aStatistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy. 615 24$aStatistics in Business, Management, Economics, Finance, Insurance. 615 7$aMineria de dades 615 7$aR (Llenguatge de programació) 615 7$aEstadística 615 7$aMetodologia de les cičncies socials 676 $a001.433 700 $aMatter$b Ulrich$01840365 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 912 $a9911020417803321 996 $aAn Introduction to Web Mining$94419893 997 $aUNINA