LEADER 03111nam 2200613 a 450 001 9910779912903321 005 20230124181334.0 010 $a1-84964-089-0 010 $a0-585-42609-0 035 $a(CKB)111056486518430 035 $a(StDuBDS)AH22933391 035 $a(SSID)ssj0000138402 035 $a(PQKBManifestationID)12000619 035 $a(PQKBTitleCode)TC0000138402 035 $a(PQKBWorkID)10100597 035 $a(PQKB)10047063 035 $a(SSID)ssj0000517828 035 $a(PQKBManifestationID)12207610 035 $a(PQKBTitleCode)TC0000517828 035 $a(PQKBWorkID)10492165 035 $a(PQKB)10640233 035 $a(MiAaPQ)EBC3386261 035 $a(Au-PeEL)EBL3386261 035 $a(CaPaEBR)ebr10479767 035 $a(CaONFJC)MIL987806 035 $a(OCoLC)50983987 035 $a(EXLCZ)99111056486518430 100 $a19991028d2000 uy 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt 182 $cc 183 $acr 200 14$aThe dialogue of negation$b[electronic resource] $edebates on hegemony in Russia and the West /$fJeremy Lester 210 $aLondon ;$aSterling, Va. $cPluto Press$d2000 215 $a1 online resource (240 p.) 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a0-7453-1629-8 311 $a0-7453-1630-1 320 $aIncludes bibliographical references and index. 330 $bThe dialogue between large elements of the Western and the Soviet/Russian left has all too often been one of negation rather than affirmation. The Dialogue of Negation pursues this argument and examines the conceptual and strategic richness of hegemony, providing an overview of the key debates which have shaped its historical development. Jeremy Lester situates the modern evolution of hegemony within an East-West dimension and focuses in particular on the deep-seated difficulties and incompatibilities of much of this interaction. Lester offers a defence of Gramsci's understanding of hegemony as a key element of the revolutionary class struggle. He acknowledges Gramsci's own disputes within the Marxist domain, and celebrates the theoretical and practical legacy he bequeathed to those who continue the struggle to replace capitalism with socialism. Lester provides a critical defence of modernity against the challenge of postmodernity, arguing that it is only within the parameters of modernity that a meaningful form of socialism can succeed. He seeks to highlight the inconsistencies and illogicalities of those theorists who see the transition to some kind of postmodern condition as offering new possibilities for the transcendence of capitalism. 606 $aPower (Social sciences) 606 $aPolitical science$xPhilosophy 615 0$aPower (Social sciences) 615 0$aPolitical science$xPhilosophy. 676 $a320.1/092 700 $aLester$b Jeremy$01526677 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910779912903321 996 $aThe dialogue of negation$93768876 997 $aUNINA LEADER 05364nam 2200649Ia 450 001 9910784656403321 005 20200520144314.0 010 $a1-281-00538-X 010 $a9786611005382 010 $a0-08-049100-6 035 $a(CKB)1000000000364038 035 $a(EBL)294574 035 $a(OCoLC)437181594 035 $a(SSID)ssj0000135046 035 $a(PQKBManifestationID)11146466 035 $a(PQKBTitleCode)TC0000135046 035 $a(PQKBWorkID)10056921 035 $a(PQKB)10256503 035 $a(Au-PeEL)EBL294574 035 $a(CaPaEBR)ebr10186413 035 $a(CaONFJC)MIL100538 035 $a(MiAaPQ)EBC294574 035 $a(EXLCZ)991000000000364038 100 $a20060718d2007 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aData preparation for data mining using SAS$b[electronic resource] /$fMamdouh Refaat 210 $aAmsterdam ;$aBoston $cMorgan Kaufmann Publishers$dc2007 215 $a1 online resource (425 p.) 225 1 $aThe Morgan Kaufmann series in data management systems 300 $aDescription based upon print version of record. 311 $a0-12-373577-7 320 $aIncludes bibliographical references (p. 373-374) and index. 327 $aFront Cover; Data Preparation for Data Mining Using SAS; Copyright Page; Contents; List of Figures; List of Tables; Preface; CHAPTER 1. INTRODUCTION; 1.1 The Data Mining Process; 1.2 Methodologies of Data Mining; 1.3 The Mining View; 1.4 The Scoring View; 1.5 Notes on Data Mining Software; CHAPTER 2. TASKS AND DATA FLOW; 2.1 Data Mining Tasks; 2.2 Data Mining Competencies; 2.3 The Data Flow; 2.4 Types of Variables; 2.5 The Mining View and the Scoring View; 2.6 Steps of Data Preparation; CHAPTER 3. REVIEW OF DATA MINING MODELING TECHNIQUES; 3.1 Introduction; 3.2 Regression Models 327 $a3.3 Decision Trees3.4 Neural Networks; 3.5 Cluster Analysis; 3.6 Association Rules; 3.7 Time Series Analysis; 3.8 Support Vector Machines; CHAPTER 4. SAS MACROS: A QUICK START; 4.1 Introduction:Why Macros?; 4.2 The Basics: The Macro and Its Variables; 4.3 Doing Calculations; 4.4 Programming Logic; 4.5 Working with Strings; 4.6 Macros That Call Other Macros; 4.7 Common Macro Patterns and Caveats; 4.8 Where to Go From Here; CHAPTER 5. DATA ACQUISITION AND INTEGRATION; 5.1 Introduction; 5.2 Sources of Data; 5.3 Variable Types; 5.4 Data Rollup; 5.5 Rollup with Sums, Averages, and Counts 327 $a5.6 Calculation of the Mode5.7 Data Integration; CHAPTER 6. INTEGRITY CHECKS; 6.1 Introduction; 6.2 Comparing Datasets; 6.3 Dataset Schema Checks; 6.4 Nominal Variables; 6.5 Continuous Variables; CHAPTER 7. EXPLORATORY DATA ANALYSIS; 7.1 Introduction; 7.2 Common EDA Procedures; 7.3 Univariate Statistics; 7.4 Variable Distribution; 7.5 Detection of Outliers; 7.6 Testing Normality; 7.7 Cross-tabulation; 7.8 Investigating Data Structures; CHAPTER 8. SAMPLING AND PARTITIONING; 8.1 Introduction; 8.2 Contents of Samples; 8.3 Random Sampling; 8.4 Balanced Sampling; 8.5 Minimum Sample Size 327 $a8.6 Checking Validity of SampleCHAPTER 9. DATA TRANSFORMATIONS; 9.1 Raw and Analytical Variables; 9.2 Scope of Data Transformations; 9.3 Creation of New Variables; 9.4 Mapping of Nominal Variables; 9.5 Normalization of Continuous Variables; 9.6 Changing the Variable Distribution; CHAPTER 10. BINNING AND REDUCTION OF CARDINALITY; 10.1 Introduction; 10.2 Cardinality Reduction; 10.3 Binning of Continuous Variables; CHAPTER 11. TREATMENT OF MISSING VALUES; 11.1 Introduction; 11.2 Simple Replacement; 11.3 Imputing Missing Values; 11.4 Imputation Methods and Strategy 327 $a11.5 SAS Macros for Multiple Imputation11.6 Predicting Missing Values; CHAPTER 12. PREDICTIVE POWER AND VARIABLE REDUCTION I; 12.1 Introduction; 12.2 Metrics of Predictive Power; 12.3 Methods of Variable Reduction; 12.4 Variable Reduction: Before or During Modeling; CHAPTER 13. ANALYSIS OF NOMINAL AND ORDINAL VARIABLES; 13.1 Introduction; 13.2 Contingency Tables; 13.3 Notation and Definitions; 13.4 Contingency Tables for Binary Variables; 13.5 Contingency Tables for Multicategory Variables; 13.6 Analysis of Ordinal Variables; 13.7 Implementation Scenarios 327 $aCHAPTER 14. ANALYSIS OF CONTINUOUS VARIABLES 330 $aAre you a data mining analyst, who spends up to 80% of your time assuring data quality, then preparing that data for developing and deploying predictive models? And do you find lots of literature on data mining theory and concepts, but when it comes to practical advice on developing good mining views find little "how to? information? And are you, like most analysts, preparing the data in SAS?This book is intended to fill this gap as your source of practical recipes. It introduces a framework for the process of data preparation for data mining, and presents the detailed implementation o 410 0$aMorgan Kaufmann series in data management systems. 606 $aData mining 615 0$aData mining. 676 $a005.74 676 $a006.3/12 22 676 $a006.312 700 $aRefaat$b Mamdouh$01497173 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910784656403321 996 $aData preparation for data mining using SAS$93722223 997 $aUNINA