Predictive analytics and data mining : concepts and practice with RapidMiner /

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Put Predictive Analytics into Action Learn the basics of Predictive Analysis and Data Mining through an easy to understand conceptual framework and immediately practice the concepts learned using the open source RapidMiner tool. Whether you are brand new to Data Mining or working on your tenth proje...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Kotu, Vijay (Autor), Deshpande, Balachandre (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Amsterdam : Elsevier Ltd., [2014]
Temas:
Data mining.
Consumer behavior.
Exploration de donn�ees (Informatique)
Consommateurs > Comportement.
COMPUTERS > General.
Consumer behavior
Data mining
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Front Cover
  • Predictive Analyticsand Data Mining
  • Copyright
  • Dedication
  • Contents
  • Foreword
  • Preface
  • WHY THIS BOOK?
  • WHO CAN USE THIS BOOK?
  • Acknowledgments
  • Chapter 1 -Introduction
  • 1.1 WHAT DATA MINING IS
  • 1.2 WHAT DATA MINING IS NOT
  • 1.3 THE CASE FOR DATA MINING
  • 1.4 TYPES OF DATA MINING
  • 1.5 DATA MINING ALGORITHMS
  • 1.6 ROADMAP FOR UPCOMING CHAPTERS
  • REFERENCES
  • Chapter 2
  • Data Mining Process
  • 2.1 PRIOR KNOWLEDGE
  • 2.2 DATA PREPARATION
  • 2.3 MODELING
  • 2.4 APPLICATION
  • 2.5 KNOWLEDGE
  • WHAT�a�?S NEXT?REFERENCES
  • Chapter 3
  • Data Exploration
  • 3.1 OBJECTIVES OF DATA EXPLORATION
  • 3.2 DATA SETS
  • 3.3 DESCRIPTIVE STATISTICS
  • 3.4 DATA VISUALIZATION
  • 3.5 ROADMAP FOR DATA EXPLORATION
  • REFERENCES
  • Chapter 4
  • Classification
  • 4.1 DECISION TREES
  • 4.2 RULE INDUCTION
  • 4.3 K-NEAREST NEIGHBORS
  • 4.4 NA�A?VE BAYESIAN
  • 4.5 ARTIFICIAL NEURAL NETWORKS
  • 4.6 SUPPORT VECTOR MACHINES
  • 4.7 ENSEMBLE LEARNERS
  • REFERENCES
  • Chapter 5
  • Regression Methods
  • 5.1 LINEAR REGRESSION
  • 5.2 LOGISTIC REGRESSION
  • CONCLUSION
  • 8.3 LIFT CURVES8.4 EVALUATING THE PREDICTIONS: IMPLEMENTATION
  • CONCLUSION
  • REFERENCES
  • Chapter 9
  • Text Mining
  • 9.1 HOW TEXT MINING WORKS
  • 9.2 IMPLEMENTING TEXT MINING WITH CLUSTERING AND CLASSIFICATION
  • CONCLUSION
  • REFERENCES
  • Chapter 10
  • Time Series Forecasting
  • 10.1 DATA-DRIVEN APPROACHES
  • 10.2 MODEL-DRIVEN FORECASTING METHODS
  • CONCLUSION
  • REFERENCES
  • Chapter 11
  • Anomaly Detection
  • 11.1 ANOMALY DETECTION CONCEPTS
  • 11.3 DENSITY-BASED OUTLIER DETECTION
  • 11.4 LOCAL OUTLIER FACTOR
  • CONCLUSION
  • REFERENCES
  • Chapter 12
  • Feature Selection12.1 CLASSIFYING FEATURE SELECTION METHODS
  • 12.2 PRINCIPAL COMPONENT ANALYSIS
  • 12.3 INFORMATION THEORY�a�?BASED FILTERING FOR NUMERIC DATA
  • CATEGORICAL DATA
  • 12.5 WRAPPER-TYPE FEATURE SELECTION
  • CONCLUSION
  • REFERENCES
  • Chapter 13
  • Getting Started with RapidMiner
  • 13.1 USER INTERFACE AND TERMINOLOGY
  • 13.2 DATA IMPORTING AND EXPORTING TOOLS
  • 13.3 DATA VISUALIZATION TOOLS
  • 13.4 DATA TRANSFORMATION TOOLS
  • 13.5 SAMPLING AND MISSING VALUE TOOLS
  • CONCLUSION
  • REFERENCES

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