LEADER 00950cam0-2200313---450- 001 990005666840403321 005 20101015115939.0 035 $a000566684 035 $aFED01000566684 035 $a(Aleph)000566684FED01 035 $a000566684 100 $a19990604d1925----km-y0itay50------ba 101 0 $aita 102 $aIT 105 $ay-------001yy 200 1 $a<>formazione dell'Impero Britannico$fGuido De Ruggiero 210 $aPadova$cLa Litotopo$d1925 215 $a37 p.$d26 cm 300 $aEstratto dall'opera "L'Europa nel secolo XIX" vol. 1.: Storia politica 610 0 $aInghilterra$aStoria$aOrigini dell'impero 676 $a942.05 700 1$aDe Ruggiero,$bGuido$f<1888-1948>$011779 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990005666840403321 952 $a942.05 DER 1$bIST.ST.FIL. 5648$fFLFBC 959 $aFLFBC 996 $aFormazione dell'Impero Britannico$9600307 997 $aUNINA LEADER 01410nam2-2200397li-450 001 990000209950203316 005 20180312154907.0 010 $a3-540-64892-5 035 $a0020995 035 $aUSA010020995 035 $a(ALEPH)000020995USA01 035 $a0020995 100 $a20001109d1998----km-y0itay0103----ba 101 0 $aeng 102 $aGW 200 1 $aAdvances in cryptology-CRYPTO'98$e18th Annual international cryptology conference Santa Barbara, California, USA, August 1998$eProceedings$fHugo Krawczyk (ed.) 210 $aBerlin [etc.]$cSringer-Verlag$dcopyr.1998 215 $aXII, 517 p.$cill.$d24 cm 225 2 $aLecture notes in computer science$v1462 410 0$10010020264$12001$aLecture notes in computer science 610 1 $acongressi$asanta barbara$a1998 610 1 $acrittografia$acongressi$a1998 676 $a00582$9Cifraggio dei dati 702 1$aKrawczyk,$bHugo 710 12$aAnnual international cryptology conference$d18.$eSanta Barbara$f1998$0754785 801 $aSistema bibliotecario di Ateneo dell' Università di Salerno$gRICA 912 $a990000209950203316 951 $a001 LNCS (1462)$b0022659 959 $aBK 969 $aSCI 979 $c19981014 979 $c20001110$lUSA01$h1714 979 $c20020403$lUSA01$h1629 979 $aPATRY$b90$c20040406$lUSA01$h1615 996 $aAdvances in cryptology-CRYPTO'98$91518995 997 $aUNISA LEADER 05575nam 2200709 450 001 9910460169903321 005 20200520144314.0 010 $a1-118-86870-6 010 $a1-118-86867-6 035 $a(CKB)3710000000359043 035 $a(EBL)1895687 035 $a(SSID)ssj0001437790 035 $a(PQKBManifestationID)11774104 035 $a(PQKBTitleCode)TC0001437790 035 $a(PQKBWorkID)11377402 035 $a(PQKB)10669391 035 $a(MiAaPQ)EBC1895687 035 $a(CaSebORM)9781118868706 035 $a(PPN)197577806 035 $a(Au-PeEL)EBL1895687 035 $a(CaPaEBR)ebr11024581 035 $a(CaONFJC)MIL770045 035 $a(OCoLC)907093982 035 $a(EXLCZ)993710000000359043 100 $a20150308h20152015 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$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$d©2015 215 $a1 online resource (827 p.) 225 1 $aWiley Series on Methods and Applications in Data Mining 300 $aDescription based upon print version of record. 311 $a1-118-11619-4 320 $aIncludes bibliographical references and index. 327 $aCover; Contents; Preface; 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; 1.4.1 CRISP-DM: The Six Phases; 1.5 Fallacies of Data Mining; 1.6 What Tasks Can Data Mining Accomplish; 1.6.1 Description; 1.6.2 Estimation; 1.6.3 Prediction; 1.6.4 Classification; 1.6.5 Clustering; 1.6.6 Association; The R Zone; R References; Exercises 327 $aChapter 2 Data Preprocessing2.1 Why do We Need to Preprocess the Data?; 2.2 Data Cleaning; 2.3 Handling Missing Data; 2.4 Identifying Misclassifications; 2.5 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 327 $a2.17 Adding an Index Field2.18 Removing Variables that are not Useful; 2.19 Variables that Should Probably not be Removed; 2.20 Removal of Duplicate Records; 2.21 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 327 $a3.8 Binning Based on Predictive Value3.9 Deriving New Variables: Flag Variables; 3.10 Deriving New Variables: Numerical Variables; 3.11 Using EDA to Investigate Correlated Predictor Variables; 3.12 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?; 4.4.1 The Eigenvalue Criterion; 4.4.2 The Proportion of Variance Explained Criterion; 4.4.3 The Minimum Communality Criterion 327 $a4.4.4 The Scree Plot Criterion4.5 Profiling the Principal Components; 4.6 Communalities; 4.6.1 Minimum Communality Criterion; 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; 4.12 An Example of a User-Defined Composite; The R Zone; R References; Exercises; Part II Statistical Analysis; Chapter 5 Univariate Statistical Analysis; 5.1 Data Mining Tasks in Discovering Knowledge in Data; 5.2 Statistical Approaches to Estimation and Prediction; 5.3 Statistical Inference 327 $a5.4 How Confident are We in Our Estimates? 330 $a Learn methods of data analysis and their application to real-world data sets 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. 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