{"product_id":"data-mining-the-web-uncovering-patterns-in-web-content-structure-and-usage-hardback-9780471666554","title":"Data Mining the Web; Uncovering Patterns in Web Content, Structure, and Usage (Hardback) 9780471666554","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eData Mining the Web\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eUncovering Patterns in Web Content, Structure, and Usage\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eZdravko Markov (Author), Daniel T. Larose (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9780471666554, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 18 May 2007\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e240 pages, Drawings: 37 B\u0026amp;W, 0 Color; Screen captures: 11 B\u0026amp;W, 0 Color; Tables: 45 B\u0026amp;W, 0 Color\u003cbr\u003e23.9 x 16.4 x 2.1 cm, 0.544 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cem\u003e\u003cfont size=\"3\"\u003e\"…it has to be noted that this book is an excellent resource for conducting Web mining lectures or single units within Data mining class. The data can be used for small as well as quite comprehensive business intelligence projects. The book's content is easy to access; even students with very basic statistical skills can get the flavor of the intriguing aspects of Web mining.\" (\u003ci\u003eJournal of Statistical Software\u003c\/i\u003e, April 2008)  \u003cp\u003e\"…highlight[s] the exciting research related to data mining the Web…a detailed summary of the current state of the art.\" (\u003ci\u003eCHOICE\u003c\/i\u003e, December 2007)\u003c\/p\u003e \u003cp\u003e\"I can say I really enjoyed reading this book…a great educational resource for students and teachers.\" (\u003ci\u003eInformation Retrieval\u003c\/i\u003e, 2008)\u003c\/p\u003e\u003c\/font\u003e\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003eThis book introduces the reader to methods of data mining on the web, including uncovering patterns in web content (classification, clustering, language processing), structure (graphs, hubs, metrics), and usage (modeling, sequence analysis, performance).\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003ePREFACE.  \u003cp\u003e\u003cb\u003ePART I: WEB STRUCTURE MINING.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1 INFORMATION RETRIEVAL AND WEB SEARCH.\u003c\/p\u003e \u003cp\u003eWeb Challenges.\u003c\/p\u003e \u003cp\u003eWeb Search Engines.\u003c\/p\u003e \u003cp\u003eTopic Directories.\u003c\/p\u003e \u003cp\u003eSemantic Web.\u003c\/p\u003e \u003cp\u003eCrawling the Web.\u003c\/p\u003e \u003cp\u003eWeb Basics.\u003c\/p\u003e \u003cp\u003eWeb Crawlers.\u003c\/p\u003e \u003cp\u003eIndexing and Keyword Search.\u003c\/p\u003e \u003cp\u003eDocument Representation.\u003c\/p\u003e \u003cp\u003eImplementation Considerations.\u003c\/p\u003e \u003cp\u003eRelevance Ranking.\u003c\/p\u003e \u003cp\u003eAdvanced Text Search.\u003c\/p\u003e \u003cp\u003eUsing the HTML Structure in Keyword Search.\u003c\/p\u003e \u003cp\u003eEvaluating Search Quality.\u003c\/p\u003e \u003cp\u003eSimilarity Search.\u003c\/p\u003e \u003cp\u003eCosine Similarity.\u003c\/p\u003e \u003cp\u003eJaccard Similarity.\u003c\/p\u003e \u003cp\u003eDocument Resemblance.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003eExercises.\u003c\/p\u003e \u003cp\u003e2 HYPERLINK-BASED RANKING.\u003c\/p\u003e \u003cp\u003eIntroduction.\u003c\/p\u003e \u003cp\u003eSocial Networks Analysis.\u003c\/p\u003e \u003cp\u003ePageRank.\u003c\/p\u003e \u003cp\u003eAuthorities and Hubs.\u003c\/p\u003e \u003cp\u003eLink-Based Similarity Search.\u003c\/p\u003e \u003cp\u003eEnhanced Techniques for Page Ranking.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003eExercises.\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART II: WEB CONTENT MINING.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3 CLUSTERING.\u003c\/p\u003e \u003cp\u003eIntroduction.\u003c\/p\u003e \u003cp\u003eHierarchical Agglomerative Clustering.\u003c\/p\u003e \u003cp\u003ek-Means Clustering.\u003c\/p\u003e \u003cp\u003eProbabilty-Based Clustering.\u003c\/p\u003e \u003cp\u003eFinite Mixture Problem.\u003c\/p\u003e \u003cp\u003eClassification Problem.\u003c\/p\u003e \u003cp\u003eClustering Problem.\u003c\/p\u003e \u003cp\u003eCollaborative Filtering (Recommender Systems).\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003eExercises.\u003c\/p\u003e \u003cp\u003e4 EVALUATING CLUSTERING.\u003c\/p\u003e \u003cp\u003eApproaches to Evaluating Clustering.\u003c\/p\u003e \u003cp\u003eSimilarity-Based Criterion Functions.\u003c\/p\u003e \u003cp\u003eProbabilistic Criterion Functions.\u003c\/p\u003e \u003cp\u003eMDL-Based Model and Feature Evaluation.\u003c\/p\u003e \u003cp\u003eMinimum Description Length Principle.\u003c\/p\u003e \u003cp\u003eMDL-Based Model Evaluation.\u003c\/p\u003e \u003cp\u003eFeature Selection.\u003c\/p\u003e \u003cp\u003eClasses-to-Clusters Evaluation.\u003c\/p\u003e \u003cp\u003ePrecision, Recall, and F-Measure.\u003c\/p\u003e \u003cp\u003eEntropy.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003eExercises.\u003c\/p\u003e \u003cp\u003e5 CLASSIFICATION.\u003c\/p\u003e \u003cp\u003eGeneral Setting and Evaluation Techniques.\u003c\/p\u003e \u003cp\u003eNearest-Neighbor Algorithm.\u003c\/p\u003e \u003cp\u003eFeature Selection.\u003c\/p\u003e \u003cp\u003eNaive Bayes Algorithm.\u003c\/p\u003e \u003cp\u003eNumerical Approaches.\u003c\/p\u003e \u003cp\u003eRelational Learning.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003eExercises.\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART III: WEB USAGE MINING.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6 INTRODUCTION TO WEB USAGE MINING.\u003c\/p\u003e \u003cp\u003eDefinition of Web Usage Mining.\u003c\/p\u003e \u003cp\u003eCross-Industry Standard Process for Data Mining.\u003c\/p\u003e \u003cp\u003eClickstream Analysis.\u003c\/p\u003e \u003cp\u003eWeb Server Log Files.\u003c\/p\u003e \u003cp\u003eRemote Host Field.\u003c\/p\u003e \u003cp\u003eDate\/Time Field.\u003c\/p\u003e \u003cp\u003eHTTP Request Field.\u003c\/p\u003e \u003cp\u003eStatus Code Field.\u003c\/p\u003e \u003cp\u003eTransfer Volume (Bytes) Field.\u003c\/p\u003e \u003cp\u003eCommon Log Format.\u003c\/p\u003e \u003cp\u003eIdentification Field.\u003c\/p\u003e \u003cp\u003eAuthuser Field.\u003c\/p\u003e \u003cp\u003eExtended Common Log Format.\u003c\/p\u003e \u003cp\u003eReferrer Field.\u003c\/p\u003e \u003cp\u003eUser Agent Field.\u003c\/p\u003e \u003cp\u003eExample of a Web Log Record.\u003c\/p\u003e \u003cp\u003eMicrosoft IIS Log Format.\u003c\/p\u003e \u003cp\u003eAuxiliary Information.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003eExercises.\u003c\/p\u003e \u003cp\u003e7 PREPROCESSING FOR WEB USAGE MINING.\u003c\/p\u003e \u003cp\u003eNeed for Preprocessing the Data.\u003c\/p\u003e \u003cp\u003eData Cleaning and Filtering.\u003c\/p\u003e \u003cp\u003ePage Extension Exploration and Filtering.\u003c\/p\u003e \u003cp\u003eDe-Spidering the Web Log File.\u003c\/p\u003e \u003cp\u003eUser Identification.\u003c\/p\u003e \u003cp\u003eSession Identification.\u003c\/p\u003e \u003cp\u003ePath Completion.\u003c\/p\u003e \u003cp\u003eDirectories and the Basket Transformation.\u003c\/p\u003e \u003cp\u003eFurther Data Preprocessing Steps.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003eExercises.\u003c\/p\u003e \u003cp\u003e8 EXPLORATORY DATA ANALYSIS FOR WEB USAGE MINING.\u003c\/p\u003e \u003cp\u003eIntroduction.\u003c\/p\u003e \u003cp\u003eNumber of Visit Actions.\u003c\/p\u003e \u003cp\u003eSession Duration.\u003c\/p\u003e \u003cp\u003eRelationship between Visit Actions and Session Duration.\u003c\/p\u003e \u003cp\u003eAverage Time per Page.\u003c\/p\u003e \u003cp\u003eDuration for Individual Pages.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003eExercises.\u003c\/p\u003e \u003cp\u003e9 MODELING FOR WEB USAGE MINING: CLUSTERING, ASSOCIATION, AND CLASSIFICATION.\u003c\/p\u003e \u003cp\u003eIntroduction.\u003c\/p\u003e \u003cp\u003eModeling Methodology.\u003c\/p\u003e \u003cp\u003eDefinition of Clustering.\u003c\/p\u003e \u003cp\u003eThe BIRCH Clustering Algorithm.\u003c\/p\u003e \u003cp\u003eAffinity Analysis and the A Priori Algorithm.\u003c\/p\u003e \u003cp\u003eDiscretizing the Numerical Variables: Binning.\u003c\/p\u003e \u003cp\u003eApplying the A Priori Algorithm to the CCSU Web Log Data.\u003c\/p\u003e \u003cp\u003eClassification and Regression Trees.\u003c\/p\u003e \u003cp\u003eThe C4.5 Algorithm.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003eExercises.\u003c\/p\u003e \u003cp\u003eINDEX.\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Computer networking \u0026amp; communications [\u003ca title=\"See our other books on Computer networking \u0026amp; communications\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Computer%20networking%20\u0026amp;%20communications%20%5BUT%5D%22\"\u003eUT\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley-Interscience","offers":[{"title":"Brand New","offer_id":52298026189080,"sku":"9780471666554","price":83.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9780471666554.jpg?v=1781731583","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/data-mining-the-web-uncovering-patterns-in-web-content-structure-and-usage-hardback-9780471666554","provider":"Freshly Printed Books","version":"1.0","type":"link"}