Educational Data Mining with R and Rattle.

Print version record.

Cover; Half Titlle page; River Publishers Series Page; Title Page -- Educational Data Mining with R and Rattle; Copyright Page; Contents; Foreword; Preface; Acknowledgment; List of Figures; List of Tables; List of Abbreviations; Chapter 1 -- Introduction; 1.1 Introduction; 1.2 Data Mining; 1.2.1 System Architecture; 1.2.2 Mining Process; 1.2.3 Functions and Products; 1.2.4 Significance and Applications; 1.3 Educational Data Mining-An Area under the Umbrella of Data Mining; 1.3.1 EDMTasks; 1.3.2 Techniques; 1.4 Research Problem; 1.4.1 Research Motivation; 1.4.2 Problem Statement.

1.4.3 Objectives1.5 R Data Mining Tool; 1.5.1 R Installation; 1.5.2 R Mining; 1.6 Rattle Data Mining Tool; 1.6.1 Rattle Installation; 1.6.2 Loading Rattle Package; 1.7 Reason for R and Rattle; Chapter 2 -- Emerging Research Directions in Educational Data Mining; 2.1 Introduction; 2.2 Prior Art Vis-à-vis of Research; 2.2.1 Educational Data Mining; 2.2.2 Data Mining Using R; 2.2.3 Mining Students' Academic Performance; 2.2.4 Factors Affecting on Students' Academic Performance; 2.2.5 Evaluation of Student Performance; 2.2.6 Knowledge Management System; 2.2.7 Placement Chance Prediction.

2.2.8 Mining Association Rules in Student's Data2.2.9 Clustering Data Mining; 2.2.10 Prediction for Student's Performance Using Classification Method; 2.2.11 Classification Techniques; 2.2.12 Educational Data Mining Model Using Rattle; 2.3 Conclusion; Chapter 3 -- Design Aspects and Developmental Framework of the System; 3.1 Introduction; 3.2 EDM Phases and Research Framework; 3.3 Methods of Educational Data Mining; 3.4 Algorithms and Tools; 3.5 Data Mining Process; 3.5.1 Data Collection; 3.5.2 Data Preprocessing and Transformation; 3.5.3 R Packages and Functions for Data Mining.

3.5.4 Result Evaluation and Knowledge Presentation3.6 Working with Data; 3.7 Research Methodology; 3.8 Loading and Exploring Data-Exploratory Data Analysis; 3.9 Interactive Graphics and Data Visualization; 3.10 Conclusion; Chapter 4 -- Model Development-Building Classifiers; 4.1 Introduction-Descriptive and Predictive Analytics; 4.2 Predictive Analytics; 4.3 Dataset and Class Labels; 4.4 Classification Framework and Process; 4.5 Predicting Students' Performance; 4.6 Classification and Predictive Modeling in R and Rattle; 4.7 Decision Tree Modeling; 4.7.1 Decision Tree Implementation in R.

4.7.2 Decision Tree in Rattle4.8 Artificial Neural Network Classifier; 4.9 Naive Bayes Classifier; 4.10 Random Forest Modeling; 4.10.1 Random Forest Model in R; 4.10.2 Random Forest Implementation in Rattle; 4.11 Model Selection and Deployment; 4.11.1 Model Evaluation in R; 4.11.2 Model Evaluation in Rattle; 4.12 Conclusion; Chapter 5 -- Educational Data Analysis: Clustering Approach; 5.1 Introduction; 5.2 Clustering in Educational Data Mining; 5.3 Experimental Setup; 5.4 Clustering Techniques; 5.5 Classification via Clustering-Design Framework; 5.6 Cluster Analysis in R and Rattle.

5.7 K-means Clustering.

Includes bibliographical references and index.

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