Predictive analytics and data mining : concepts and practice with RapidMiner /
"Put predictive analytics into action. Learn the basics of Predictive analytics 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 ten...
Guardado en:
| Autor principal: | |
|---|---|
| Otros Autores: | |
| Formato: | Libro |
| Lenguaje: | Inglés |
| Publicado: |
Amsterdam :
Elsevier : Morgan Kaufmann,
c2015.
|
| Materias: | |
| Aporte de: | Registro referencial: Solicitar el recurso aquí |
| LEADER | 04887cam a2200337Ia 4500 | ||
|---|---|---|---|
| 001 | 99743024604151 | ||
| 005 | 20190530152816.0 | ||
| 008 | 150113s2015 ne a b 001 0 eng d | ||
| 010 | |a 2014952930 | ||
| 020 | |a 9780128014608 | ||
| 020 | |a 0128014601 | ||
| 020 | |a 9780128016503 | ||
| 020 | |a 0128016507 | ||
| 035 | |a (OCoLC)900080446 | ||
| 035 | |a (OCoLC)ocn900080446 | ||
| 040 | |a IND |c IND |d YDXCP |d BTCTA |d UKMGB |d LND |d OCLCF |d CHVBK |d NJT |d CNNAI |d OCLCQ |d I8M |d OCLCO |d MMV |d THHCU |d OCLCQ |d MOF |d DLC |d OCLCQ |d OCLCA |d U@S | ||
| 049 | |a U@SA | ||
| 050 | 4 | |a QA76.9.D343 |b K67 2015 | |
| 082 | 0 | 4 | |a 006.312 K87 2015 |
| 100 | 1 | |a Kotu, Vijay. | |
| 245 | 1 | 0 | |a Predictive analytics and data mining : |b concepts and practice with RapidMiner / |c Vijay Kotu, Bala Deshpande. |
| 260 | |a Amsterdam : |b Elsevier : |b Morgan Kaufmann, |c c2015. | ||
| 300 | |a xix, 425 p. : |b il. ; |c 24 cm. | ||
| 504 | |a Incluye referencias bibliográficas e índice. | ||
| 505 | 0 | |a Foreword -- Preface -- 1. Introduction: What Data Mining Is -- What Data Mining Is Not -- Case for Data Mining -- Types of Data Mining -- Data Mining Algorithms -- Roadmap for Upcoming Chapters -- 2. Data Mining Process: Prior Knowledge -- Data Preparation -- Modeling -- Application -- Knowledge -- 3. Data exploration: Objectives of Data Exploration -- Data Sets -- Descriptive Statistics -- Data Visualization -- Roadmap for Data Exploration -- 4. Classification: Decision Trees -- Rule Induction -- k-Nearest Neighbors -- Naïve Bayesian -- Artificial Neural Networks -- Support Vector Machines -- Ensemble Learners -- 5. Regression Methods: Linear Regression -- Logistic Regression -- 6. Association Analysis: Concepts of Mining Association Rules -- Apriori Algorithm -- FP-Growth Algorithm -- 7. Clustering: Types of Clustering Techniques -- k-Means Clustering -- DBSCAN Clustering -- Self-Organizing Maps -- 8. Model Evaluation: Confusion Matrix (or Truth Table) -- Receiver Operator Characteristic (ROC) Curves and Area under the Curve (AUC) -- Lift Curves -- Evaluating the Predictions: Implementation -- 9. Text Mining: How Text Mining Works -- Implementing Text Mining with Clustering and Classification -- 10. Time series forecasting: Data-Driven Approaches -- Model-Driven Forecasting Methods -- 11. Anomaly Detection: Anomaly Detection Concepts -- Distance-Based Outlier Detection -- Density-Based Outlier Detection -- Local Outlier Factor -- 12. Feature Selection: Classifying Feature Selection Methods -- Principal Component Analysis -- Information Theory-Based Filtering for Numeric Data -- Chi-Square-Based Filtering for Categorical Data -- Wrapper-Type Feature Selection -- 13. Getting Started with RapidMiner: User Interface and Terminology -- Data Importing and Exporting Tools -- Data Visualization Tools -- Data Transformation Tools -- Sampling and Missing Value Tools -- Optimization Tools -- Comparison of Data Mining Algorithms. | |
| 520 | |a "Put predictive analytics into action. Learn the basics of Predictive analytics 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 users, data analysts, business analysts, business intelligence and data warehousing professionals and for anyone who wants to learn Data Mining. You'll be able to: 1. Gain the necessary knowledge of different data mining techniques, so that you can select the right technique for a given data problem and create a general purpose analytics process. 2. Get up and running fast with more than two dozen commonly used powerful algorithms for predictive analytics using practical use cases. 3. Implement a simple step-by-step process for predicting an outcome or discovering hidden relationships from the data using RapidMiner, an open source GUI based data mining tool. Predictive analytics and data mining techniques covered: Exploratory Data Analysis, Visualization, Decision trees, Rule induction, k-Nearest Neighbors, Naïve Bayesian, Artificial Neural Networks, Support Vector machines, Ensemble models, Bagging, Boosting, Random Forests, Linear regression, Logistic regression, Association analysis using Apriori and FP Growth, K-Means clustering, Density based clustering, Self Organizing Maps, Text Mining, Time series forecasting, Anomaly detection and Feature selection." --Contratapa. | ||
| 650 | 0 | |a Data mining. | |
| 650 | 0 | |a Consumer behavior. | |
| 650 | 7 | |a Minería de datos. |2 UDESA | |
| 650 | 7 | |a Conducta del consumidor. |2 UDESA | |
| 700 | 1 | |a Deshpande, Balachandre. | |