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 données (Informatique)
Consommateurs > Comportement.
COMPUTERS > General.
Acceso en línea:Texto completo

MARC

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505 0 |a 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 
505 8 |a WHATâ€?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 NAÃ?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 
505 8 |a 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 
505 8 |a Chapter 12 -- Feature Selection12.1 CLASSIFYING FEATURE SELECTION METHODS -- 12.2 PRINCIPAL COMPONENT ANALYSIS -- 12.3 INFORMATION THEORYâ€?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 
520 |a 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 project, this book will show you how to analyze data, uncover hidden patterns and relationships to aid important decisions and predictions. Data Mining has become an essential tool for any enterprise that collects, stores and processes data as part of its operations. This book is ideal for business user. 
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650 0 |a Data mining. 
650 0 |a Consumer behavior. 
650 6 |a Exploration de données (Informatique) 
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650 7 |a Data mining.  |2 fast  |0 (OCoLC)fst00887946 
700 1 |a Deshpande, Balachandre,  |e author. 
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