Predictive analytics and data mining :: concepts and practice with RapidMiner /
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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| Hauptverfasser: | , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Amsterdam :
Elsevier Ltd.,
[2014]
|
| Schlagworte: | |
| Online-Zugang: | Volltext Volltext |
| Zusammenfassung: | 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. |
| Beschreibung: | 1 online resource |
| ISBN: | 9780128016503 0128016507 |
Internformat
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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 WHAT�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�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 THEORY�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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| author | Kotu, Vijay Deshpande, Balachandre |
| author_GND | http://id.loc.gov/authorities/names/no2015042502 |
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| contents | 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�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�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�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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| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| format | Electronic eBook |
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| indexdate | 2024-11-27T13:26:21Z |
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| publisher | Elsevier Ltd., |
| record_format | marc |
| spelling | Kotu, Vijay, author. Predictive analytics and data mining : concepts and practice with RapidMiner / Vijay Kotu, Bala Deshpande. Amsterdam : Elsevier Ltd., [2014] ©2015 1 online resource text txt rdacontent computer c rdamedia online resource cr rdacarrier Vendor-supplied metadata. 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 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 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 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 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. Data mining. http://id.loc.gov/authorities/subjects/sh97002073 Consumer behavior. http://id.loc.gov/authorities/subjects/sh87006429 Exploration de données (Informatique) Consommateurs Comportement. COMPUTERS General. bisacsh Consumer behavior fast Data mining fast Deshpande, Balachandre, author. https://id.oclc.org/worldcat/entity/E39PCjvdDq76QgMM4YYqqrBvBP http://id.loc.gov/authorities/names/no2015042502 has work: Predictive analytics and data mining (Text) https://id.oclc.org/worldcat/entity/E39PCGqh4KydvVP4ytKBkhtywK https://id.oclc.org/worldcat/ontology/hasWork Erscheint auch als: Druck-Ausgabe Kotu, Vijay. Predictive Analytics and Data Mining . Concepts and Practice with RapidMiner FWS01 ZDB-4-EBA FWS_PDA_EBA https://www.sciencedirect.com/science/book/9780128014608 Volltext FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=919334 Volltext |
| spellingShingle | Kotu, Vijay Deshpande, Balachandre Predictive analytics and data mining : concepts and practice with RapidMiner / 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 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 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 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 Data mining. http://id.loc.gov/authorities/subjects/sh97002073 Consumer behavior. http://id.loc.gov/authorities/subjects/sh87006429 Exploration de données (Informatique) Consommateurs Comportement. COMPUTERS General. bisacsh Consumer behavior fast Data mining fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh97002073 http://id.loc.gov/authorities/subjects/sh87006429 |
| title | Predictive analytics and data mining : concepts and practice with RapidMiner / |
| title_auth | Predictive analytics and data mining : concepts and practice with RapidMiner / |
| title_exact_search | Predictive analytics and data mining : concepts and practice with RapidMiner / |
| title_full | Predictive analytics and data mining : concepts and practice with RapidMiner / Vijay Kotu, Bala Deshpande. |
| title_fullStr | Predictive analytics and data mining : concepts and practice with RapidMiner / Vijay Kotu, Bala Deshpande. |
| title_full_unstemmed | Predictive analytics and data mining : concepts and practice with RapidMiner / Vijay Kotu, Bala Deshpande. |
| title_short | Predictive analytics and data mining : |
| title_sort | predictive analytics and data mining concepts and practice with rapidminer |
| title_sub | concepts and practice with RapidMiner / |
| topic | Data mining. http://id.loc.gov/authorities/subjects/sh97002073 Consumer behavior. http://id.loc.gov/authorities/subjects/sh87006429 Exploration de données (Informatique) Consommateurs Comportement. COMPUTERS General. bisacsh Consumer behavior fast Data mining fast |
| topic_facet | Data mining. Consumer behavior. Exploration de données (Informatique) Consommateurs Comportement. COMPUTERS General. Consumer behavior Data mining |
| url | https://www.sciencedirect.com/science/book/9780128014608 https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=919334 |
| work_keys_str_mv | AT kotuvijay predictiveanalyticsanddataminingconceptsandpracticewithrapidminer AT deshpandebalachandre predictiveanalyticsanddataminingconceptsandpracticewithrapidminer |