Index Contents Page No. Introduction . Data Mining & Knowledge Discovery

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1 Index Contents Page No. 1. Introduction Related Research Objective of Research Work Why Data Mining is Important Research Methodology Research Hypothesis Scope 5 2. Data Mining & Knowledge Discovery Data Mining Concepts Data Mining Process Data Mining as a Part of the Knowledge Discovery Process Models for Data Mining Goals of Data Mining and Knowledge Discovery Prediction Identification Classification Optimization Types of Knowledge Discovered during Data Mining Association Rules Classification Hierarchy Sequential Patterns Patterns within Time Series Categorization and segmentation Learning 27

2 2.7.1 Inductive Learning Supervised learning Unsupervised learning Data Mining and Data Warehousing Introduction to Data Warehousing Characteristics of Data Warehouse Benefits of Data Warehouse How Does a Data Warehouse Work Data Mining Tasks & Algorithm Association Analysis Algorithm APRIORI Classification and Prediction Bayesian Classification Decision Trees Viewing Decision Trees as Segmentation with a Purpose Applying Decision Trees to Business Where Can Decision Trees be Used Using Decision Trees for Data Preprocessing Decision tress for Prediction C4.5 Algorithm: Generating a Decision Tree Neural Networks Artificial Neural Network The Mathematical Model Neural Networks in Data Mining Applying Neural Networks to Business Neural Networks for Clustering Neural Networks for Outlier Analysis Rule Induction Applying Rule Induction to Business What is a rule 55

3 3.7.3 What to do with a Rule Discovery Deviation analysis Clustering Analysis K-means Partitional-Clustering Algorithm K-Means Clustering Outlier Analysis Data Mining in Higher Education Data Mining for Education Prediction Clustering Relationship Mining Distillation of Data for Human Judgment Scenario of Higher Education in India Data Mining: A Way to Improve Today s Higher Learning Institutions Supervised and Unsupervised modeling Application of Data Mining in Higher Education Data Mining Areas Major Data Mining Tasks that Can be Used to Find Certain Patterns The Integration of Data Mining Processes in Higher Education Topics Analysis of Data Mining Applications in Education System Predicting Alumni Pledge Uses of Data Mining in CRCT Scores Creating Meaningful Learning Outcome Typologies Use of Data Mining Techniques to Develop Institutional Typologies Academic Planning and Interventions Transfer Prediction Predicting and Clustering Persisters and Non-Persisters Predicting a Student s Performance 87

4 5.8 Improving Quality of Graduate Students by Data Mining Proposed Analysis Guideline (DM-HEDU) Data Analysis and Investigation Domain Understanding Data Understanding Data Preparation Data Mining Modeling Predictive Data Mining Models 96 Model A: Predicting Student Success Rate for Individual 96 Student Model A.1: Decision Tree and Neural Network Classification 97 Technique Model A.2: Neural Network and RBF Prediction Techniques 97 Model B: Predicting Student Success Rate for Individual 98 Lecturer Model B.1: Decision Tree and Neural Network Classification 98 Technique Model B.2: Neural Network Model Descriptive Data Mining Modeling 99 Model C: Model of Student Course Enrollment 99 Model D: Model of Lecturer Course Assignment Policy 100 Making Model E: Model of Lecturer Typologies 100 Model E.1: Cluster Number Model E.2: Cluster Number Model F: Model of Course Time Planning Analysis and Discussion Creditability of the Results Factors Affecting the Reliability of Model Reduction in the total number of data Handling missing value phase 102

5 Incompleteness and low quality of important attribute 103 value Data integration and database application Inconsistency and value error among attributes Reduction in the total number of attributes Feature Selection in data preparation phase Medium quality of attributes and their values Suggestion to Improve the Model s Quality Student s background knowledge (pre-university academic 105 information) Student s course knowledge Student s demographics knowledge Lecturer academic knowledge Lecturer s demographic knowledge Course knowledge Summary Testing & Result on Potential Application of Data Mining in Higher 107 Education 6.1 Organization of Syllabus Methodology Predicting the Registration of Students in an Educational Program Methodology Predicting Student Performance Methodology Identifying Abnormal/ Erroneous Values Result Applying Data Mining Techniques to a Management Institute The Admission Process Counseling Data Mining Techniques in admission & Counseling 116

6 6.7 Expected Benefits Data Mining & Decision Making Environment The Decision Making Environment in Higher Education Demands for Improved Decision Making Capabilities Challenges for Improving Decision Making Capabilities A Framework for Decision Making Capabilities Five Guiding Principles for Developing Decision Making 123 Capabilities Program Lifecycle Management Design Data & System Architecture The Architecture of the Data Warehouse System Dimensional Model of the HEIS Data Warehouse Data Extraction, Transformation and Loading Presenting Data Predefined Queries Detailed Ad hoc Queries Summary Ad hoc Queries Sustainable Approach for Data Warehousing at Institute Decision Support Stages Structure of Data Warehouse Higher Education and Strategic Decision Support Planning the Education Data Warehouse for Strategic Decision 138 Making Planning ETL Processes and Data Warehouse Creation Detailed Analysis Planning / Execution / Implementation Case Studies Case study one: Academic planning and interventions transfer 142 prediction Challenge 142

7 8.1.2 Solution Results Case study two: Predicting alumni pledges Challenge Solution Results The Research Approach The Implementation Data mining in Higher Education System Proposed Model Application Results and Discussion Case Study Three: Data Mining Process Data Preparations Data selection and transformation Decision Tree The ID3 Decision Tree Measuring Impurity Splitting Criteria The ID3Algoritm Results and Discussion Case study four: Course planning of higher education Methodology Factors that determining the Quality of Education System Architecture CHAID for Data Mining Link Analysis for Data Mining Decision Forest for Data Mining Course completion rate of entire PG students Conclusion Application Example 166

8 8.7.1 Conclusion Conclusion and Future Work Conclusion 9.2 Future Work Appendix A: An Introduction to Data Mining Software Tool WEKA & 174 RapidMiner WEKA 174 A.1 Description 174 A.2 Explorer 175 A.3 Regression 176 A.4 Building The Data Set for WEKA 176 A.5 Loading the Data into WEKA 177 A.6 Creating the Regression Model with WEKA 177 A.7 Interpreting the Regression Model 178 A.8 ARFF File 178 RapidMiner 179 A.1 Features 179 A.2 Purpose 180 A.3 Applications 180 A.4 Properties 180 A.5 GUI 181 Appendix B: Statistical Package for the Social Science (SPSS) 182 B.1 Statistics Included in the Base Software 182

9 B.2 The Most Popular IBM SPSS Products Include 183 Appendix C: Words & Acronyms 184 C.1 Words 184 C.2 Acronyms 188 Bibliography 190

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