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

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This book shows how to analyze data, uncover hidden patterns and relationships to aid important decisions and predictions. Topics include: exploratory data analysis; visualization; decision trees; rule induction; k-nearest neighbors; naive Bayesian; artificial neural networks; support vector machine...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Kotu, Vijay (Autor), Deshpande, Balachandre (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Waltham, MA : Morgan Kaufmann, [2015]
Temas:
Data mining.
Consumer behavior.
Exploration de données (Informatique)
Consommateurs > Comportement.
Consumer behavior
Data mining
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • Machine generated contents note: 1.1. What Data Mining Is
  • 1.2. What Data Mining Is Not
  • 1.3. Case for Data Mining
  • 1.4. Types of Data Mining
  • 1.5. Data Mining Algorithms
  • 1.6. Roadmap for Upcoming Chapters
  • 2.1. Prior Knowledge
  • 2.2. Data Preparation
  • 2.3. Modeling
  • 2.4. Application
  • 2.5. Knowledge
  • 3.1. Objectives of Data Exploration
  • 3.2. Data Sets
  • 3.3. Descriptive Statistics
  • 3.4. Data Visualization
  • 3.5. Roadmap for Data Exploration
  • 4.1. Decision Trees
  • 4.2. Rule Induction
  • 4.3. k-Nearest Neighbors
  • 4.4. Naïve Bayesian
  • 4.5. Artificial Neural Networks
  • 4.6. Support Vector Machines
  • 4.7. Ensemble Learners
  • 5.1. Linear Regression
  • 5.2. Logistic Regression
  • 6.1. Concepts of Mining Association Rules
  • 6.2. Apriori Algorithm
  • 6.3. FP-Growth Algorithm
  • 7.1. Types of Clustering Techniques
  • 7.2. k-Means Clustering
  • 7.3. DBSCAN Clustering
  • 7.4. Self-Organizing Maps
  • 8.1. Confusion Matrix (or Truth Table)
  • 8.2. Receiver Operator Characteristic (ROC) Curves and Area under the Curve (AUC)
  • 8.3. Lift Curves
  • 8.4. Evaluating the Predictions: Implementation
  • 9.1. How Text Mining Works
  • 9.2. Implementing Text Mining with Clustering and Classification
  • 10.1. Data-Driven Approaches
  • 10.2. Model-Driven Forecasting Methods
  • 11.1. Anomaly Detection Concepts
  • 11.2. Distance-Based Outlier Detection
  • 11.3. Density-Based Outlier Detection
  • 11.4. Local Outlier Factor
  • 12.1. Classifying Feature Selection Methods
  • 12.2. Principal Component Analysis
  • 12.3. Information Theory-Based Filtering for Numeric Data
  • 12.4. Chi-Square-Based Filtering for Categorical Data
  • 12.5. Wrapper-Type Feature Selection
  • 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
  • 13.6. Optimization Tools.

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