LEADER 02388oam 2200601 450 001 9910713900303321 005 20200819093658.0 035 $a(CKB)5470000002504686 035 $a(OCoLC)682137441$z(OCoLC)622332164$z(OCoLC)667913485$z(OCoLC)1153330394 035 $a(OCoLC)995470000002504686 035 $a(EXLCZ)995470000002504686 100 $a20101117d1996 ua 0 101 0 $aeng 135 $aurbn||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aStreambed-material characteristics and surface-water quality, Green Pond Brook and tributaries, Picatinny Arsenal, New Jersey, 1983-90 /$fby Donald A. Storck and Pierre J. Lacombe 210 1$aWest Trenton, New Jersey :$cU.S. Geological Survey,$d1996. 215 $a1 online resource (v, 56 pages) $cillustrations, maps +$e2 plates 225 1 $aWater-resources investigations report ;$v95-4246 300 $a"Prepared in cooperation with the U.S. Army Armament Research Development and Engineering Center." 320 $aIncludes bibliographical references (pages 30-32). 517 3 $aStreambed material characteristics and surface-water quality, Green Pond Brook and tributaries, Picatinny Arsenal, New Jersey, 1983-90 606 $aArsenals$zNew Jersey$zMorris County 606 $aRiver sediments$zNew Jersey$zMorris County 606 $aWater$xPollution$zNew Jersey$zMorris County 606 $aWater quality$zNew Jersey$zMorris County 606 $aWater$xPollution$2fast 606 $aWater quality$2fast 607 $aNew Jersey$zDover Region$2fast 615 0$aArsenals 615 0$aRiver sediments 615 0$aWater$xPollution 615 0$aWater quality 615 7$aWater$xPollution. 615 7$aWater quality. 700 $aStorck$b Donald A.$01395699 702 $aLacombe$b Pierre L. 712 02$aGeological Survey (U.S.), 712 02$aArmy Armament Research, Development, and Engineering Center (U.S.) 801 0$bOCLCE 801 1$bOCLCE 801 2$bOCLCQ 801 2$bOCLCO 801 2$bOCLCQ 801 2$bOCLCA 801 2$bOCLCF 801 2$bGPO 906 $aBOOK 912 $a9910713900303321 996 $aStreambed-material characteristics and surface-water quality, Green Pond Brook and tributaries, Picatinny Arsenal, New Jersey, 1983-90$93523651 997 $aUNINA LEADER 05294nam 2200553 450 001 9910807887903321 005 20230912174843.0 010 $a1-118-86867-6 010 $a1-118-86870-6 035 $a(CKB)24989753400041 035 $a(NjHacI)9924989753400041 035 $a(Au-PeEL)EBL1895687 035 $a(CaPaEBR)ebr11024581 035 $a(CaONFJC)MIL770045 035 $a(OCoLC)907093982 035 $a(PPN)197577806 035 $a(CaSebORM)9781118868706 035 $a(MiAaPQ)EBC1895687 035 $a(EXLCZ)9924989753400041 100 $a20150308h20152015 uy 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aData mining and predictive analytics /$fDaniel T. Larose, Chantal D. Larose 205 $aSecond edition. 210 1$aHoboken, New Jersey :$cJohn Wiley & Sons,$d2015. 210 4$d2015 215 $a1 online resource (824 pages) $cillustrations 225 1 $aWiley Series on Methods and Applications in Data Mining 320 $aIncludes bibliographical references and index. 327 $aSeries; Title Page; Copyright; Table of Contents; Dedication; Preface; What is Data Mining? What is Predictive Analytics?; Why is this Book Needed?; Who Will Benefit from this Book?; Danger! Data Mining is Easy to do Badly; "White-Box" Approach; Algorithm Walk-Throughs; Exciting New Topics; The R Zone; Appendix: Data Summarization and Visualization; The Case Study: Bringing it all Together; How the Book is Structured; The Software; Weka: The Open-Source Alternative; The Companion Web Site: www.dataminingconsultant.com; Data Mining and Predictive Analytics as a Textbook; Acknowledgments. Daniel's AcknowledgmentsChantal's Acknowledgments; Part I: Data Preparation; Chapter 1: An Introduction to Data Mining and Predictive Analytics; 1.1 What is Data Mining? What Is Predictive Analytics?; 1.2 Wanted: Data Miners; 1.3 The Need For Human Direction of Data Mining; 1.4 The Cross-Industry Standard Process for Data Mining: CRISP-DM; 1.5 Fallacies of Data Mining; 1.6 What Tasks can Data Mining Accomplish; The R Zone; R References; Exercises; Chapter 2: Data Preprocessing; 2.1 Why do We Need to Preprocess the Data?; 2.2 Data Cleaning; 2.3 Handling Missing Data. 2.4 Identifying Misclassifications2.5 Graphical Methods for Identifying Outliers; 2.6 Measures of Center and Spread; 2.7 Data Transformation; 2.8 Min-Max Normalization; 2.9 Z-Score Standardization; 2.10 Decimal Scaling; 2.11 Transformations to Achieve Normality; 2.12 Numerical Methods for Identifying Outliers; 2.13 Flag Variables; 2.14 Transforming Categorical Variables into Numerical Variables; 2.15 Binning Numerical Variables; 2.16 Reclassifying Categorical Variables; 2.17 Adding an Index Field; 2.18 Removing Variables that are not Useful; 2.19 Variables that Should Probably not be Removed. 2.20 Removal of Duplicate Records2.21 A Word About ID Fields; The R Zone; R Reference; Exercises; Chapter 3: Exploratory Data Analysis; 3.1 Hypothesis Testing Versus Exploratory Data Analysis; 3.2 Getting to Know The Data Set; 3.3 Exploring Categorical Variables; 3.4 Exploring Numeric Variables; 3.5 Exploring Multivariate Relationships; 3.6 Selecting Interesting Subsets of the Data for Further Investigation; 3.7 Using EDA to Uncover Anomalous Fields; 3.8 Binning Based on Predictive Value; 3.9 Deriving New Variables: Flag Variables; 3.10 Deriving New Variables: Numerical Variables. 3.11 Using EDA to Investigate Correlated Predictor Variables3.12 Summary of Our EDA; The R Zone; R References; Exercises; Chapter 4: Dimension-Reduction Methods; 4.1 Need for Dimension-Reduction in Data Mining; 4.2 Principal Components Analysis; 4.3 Applying PCA to the Houses Data Set; 4.4 How Many Components Should We Extract?; 4.5 Profiling the Principal Components; 4.6 Communalities; 4.7 Validation of the Principal Components; 4.8 Factor Analysis; 4.9 Applying Factor Analysis to the Adult Data Set; 4.10 Factor Rotation; 4.11 User-Defined Composites. 330 $a"This updated second edition serves as an introduction to data mining methods and models, including association rules, clustering, neural networks, logistic regression, and multivariate analysis. The authors apply a unified 'white box' approach to data mining methods and models. This approach is designed to walk readers through the operations and nuances of the various methods, using small data sets, so readers can gain an insight into the inner workings of the method under review. Chapters provide readers with hands-on analysis problems, representing an opportunity for readers to apply their newly-acquired data mining expertise to solving real problems using large, real-world data sets."-- Portion of summary from book. 410 0$aWiley series on methods and applications in data mining. 606 $aData mining 606 $aPrediction theory 615 0$aData mining. 615 0$aPrediction theory. 676 $a006.3/12 700 $aLarose$b Daniel T.$0497081 702 $aLarose$b Chantal D. 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910807887903321 996 $aData mining and predictive analytics$94025633 997 $aUNINA