{"product_id":"big-data-data-mining-and-machine-learning-9781118618042","title":"Big Data Data Mining and Machine Learning","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eWith big data analytics comes big insights into profitability    Big data is big business. But having the data and the computational power to process it isn't nearly enough to produce meaningful results.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003eexplains what it covers very well (ZDNet, September 2014)\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eForward xiii\u003c\/p\u003e \u003cp\u003ePreface xv\u003c\/p\u003e \u003cp\u003eAcknowledgments xix\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIntroduction 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eBig Data Timeline 5\u003c\/p\u003e \u003cp\u003eWhy This Topic is Relevant Now 8\u003c\/p\u003e \u003cp\u003eIs Big Data a Fad? 9\u003c\/p\u003e \u003cp\u003eWhere Using Big Data Makes a Big Difference 12\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart One The Computing Environment 23\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1 Hardware 27\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eStorage (Disk) 27\u003c\/p\u003e \u003cp\u003eCentral Processing Unit 29\u003c\/p\u003e \u003cp\u003eMemory 31\u003c\/p\u003e \u003cp\u003eNetwork 33\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2 Distributed Systems 35\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDatabase Computing 36\u003c\/p\u003e \u003cp\u003eFile System Computing 37\u003c\/p\u003e \u003cp\u003eConsiderations 39\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3 Analytical Tools 43\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWeka 43\u003c\/p\u003e \u003cp\u003eJava and JVM Languages 44\u003c\/p\u003e \u003cp\u003eR 47\u003c\/p\u003e \u003cp\u003ePython 49\u003c\/p\u003e \u003cp\u003eSAS 50\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart Two Turning Data into Business Value 53\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4 Predictive Modeling 55\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eA Methodology for Building Models 58\u003c\/p\u003e \u003cp\u003esEMMA 61\u003c\/p\u003e \u003cp\u003eBinary Classifi cation 64\u003c\/p\u003e \u003cp\u003eMultilevel Classifi cation 66\u003c\/p\u003e \u003cp\u003eInterval Prediction 66\u003c\/p\u003e \u003cp\u003eAssessment of Predictive Models 67\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5 Common Predictive Modeling Techniques 71\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eRFM 72\u003c\/p\u003e \u003cp\u003eRegression 75\u003c\/p\u003e \u003cp\u003eGeneralized Linear Models 84\u003c\/p\u003e \u003cp\u003eNeural Networks 90\u003c\/p\u003e \u003cp\u003eDecision and Regression Trees 101\u003c\/p\u003e \u003cp\u003eSupport Vector Machines 107\u003c\/p\u003e \u003cp\u003eBayesian Methods Network Classifi cation 113\u003c\/p\u003e \u003cp\u003eEnsemble Methods 124\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6 Segmentation 127\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eCluster Analysis 132\u003c\/p\u003e \u003cp\u003eDistance Measures (Metrics) 133\u003c\/p\u003e \u003cp\u003eEvaluating Clustering 134\u003c\/p\u003e \u003cp\u003eNumber of Clusters 135\u003c\/p\u003e \u003cp\u003e\u003ci\u003eK\u003c\/i\u003e‐means Algorithm 137\u003c\/p\u003e \u003cp\u003eHierarchical Clustering 138\u003c\/p\u003e \u003cp\u003eProfi ling Clusters 138\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7 Incremental Response Modeling 141\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eBuilding the Response Model 142\u003c\/p\u003e \u003cp\u003eMeasuring the Incremental Response 143\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8 Time Series Data Mining 149\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eReducing Dimensionality 150\u003c\/p\u003e \u003cp\u003eDetecting Patterns 151\u003c\/p\u003e \u003cp\u003eTime Series Data Mining in Action: Nike+ FuelBand 154\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9 Recommendation Systems 163\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWhat Are Recommendation Systems? 163\u003c\/p\u003e \u003cp\u003eWhere Are They Used? 164\u003c\/p\u003e \u003cp\u003eHow Do They Work? 165\u003c\/p\u003e \u003cp\u003eAssessing Recommendation Quality 170\u003c\/p\u003e \u003cp\u003eRecommendations in Action: SAS Library 171\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 10 Text Analytics 175\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eInformation Retrieval 176\u003c\/p\u003e \u003cp\u003eContent Categorization 177\u003c\/p\u003e \u003cp\u003eText Mining 178\u003c\/p\u003e \u003cp\u003eText Analytics in Action: Let’s Play \u003ci\u003eJeopardy! \u003c\/i\u003e180\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart Three Success Stories of Putting It All Together 193\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 11 Case Study of a Large U.S.‐Based Financial Services Company 197\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eTraditional Marketing Campaign Process 198\u003c\/p\u003e \u003cp\u003eHigh‐Performance Marketing Solution 202\u003c\/p\u003e \u003cp\u003eValue Proposition for Change 203\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 12 Case Study of a Major Health Care Provider 205\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eCAHPS 207\u003c\/p\u003e \u003cp\u003eHEDIS 207\u003c\/p\u003e \u003cp\u003eHOS 208\u003c\/p\u003e \u003cp\u003eIRE 208\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 13 Case Study of a Technology Manufacturer 215\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eFinding Defective Devices 215\u003c\/p\u003e \u003cp\u003eHow They Reduced Cost 216\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 14 Case Study of Online Brand Management 221\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 15 Case Study of Mobile Application Recommendations 225\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 16 Case Study of a High‐Tech Product Manufacturer 229\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eHandling the Missing Data 230\u003c\/p\u003e \u003cp\u003eApplication beyond Manufacturing 231\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 17 Looking to the Future 233\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eReproducible Research 234\u003c\/p\u003e \u003cp\u003ePrivacy with Public Data Sets 234\u003c\/p\u003e \u003cp\u003eThe Internet of Things 236\u003c\/p\u003e \u003cp\u003eSoftware Development in the Future 237\u003c\/p\u003e \u003cp\u003eFuture Development of Algorithms 238\u003c\/p\u003e \u003cp\u003eIn Conclusion 241\u003c\/p\u003e \u003cp\u003eAbout the Author 243\u003c\/p\u003e \u003cp\u003eAppendix 245\u003c\/p\u003e \u003cp\u003eReferences 247\u003c\/p\u003e \u003cp\u003eIndex 253\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49406895751511,"sku":"9781118618042","price":37.5,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781118618042.jpg?v=1730497476","url":"https:\/\/bookcurl.com\/products\/big-data-data-mining-and-machine-learning-9781118618042","provider":"Book Curl","version":"1.0","type":"link"}