Advanced Data Mining

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Introduction                            Download PPT Slides

1. What Motivated Data Mining? Why Is It Important? 
2. So, What Is Data Mining?
3. Data Mining--On What Kind of Data? 
4. Data Mining Functionalities—What Kinds of Patterns Can Be Mined? 
5. Are All of the Patterns Interesting? 
6. Classification of Data Mining Systems 
7. Data Mining Task Primitives 
8. Integration of a Data Mining System with a Database or Data Warehouse system 
9. Major Issues in Data Mining 
10. Data Mining Applications 
11. Data Mining System Products and Research Prototypes 
12. Social Impacts of Data Mining

Data Preprocessing          Download PPT Slides          Download PDF Math Pages File

1. Why Preprocess the Data? 
2. Descriptive Data Summarization
3. Data Cleaning
4. Data Integration and Transformation 
5. Data Reduction
6. Data Discretization and Concept Hierarchy Generation
7. Feature Selection Techniques

Mining Frequent Patterns and Associations         Download PPT Slides    

1. Basic Concepts and a Road Map 
2. Efficient and Scalable Frequent Item set Mining Methods 
3. Mining Various Kinds of Association Rules
4. Using WEKA software for finding Association Rules

 Classification and Prediction         Download PPT Slides

1. What Is Classification? What Is Prediction? 
2. Issues Regarding Classification and Prediction 
3. Classification by Decision Tree Induction          Download PPT More Slides
4. Bayesian Classification                                   Download PPT  Slides
5. Rule-Based Classification                                Download PPT  Slides
6. Prediction 
7. Accuracy and Error Measures 
8. Evaluating the Accuracy of a Classifier or Predictor 
9. Using WEKA software for data Classification
10. Using Oracle Data Mining                                      Download PPT  Slides  

         

Classification Using Lazy Learning Techniques        Download PPT Slides 

1. Tasks of concept learning and classification  
2. Features of lazy learning  
3. Similarity measures  
4. Calculate and Explain values of similarity  
5. Formulate lazy learning tasks 
6. Lazy learning algorithms : (Instance-based learning and kNN-learning)       
7. Apply the lazy learning algorithms to learning tasks, (Classification task) 
8. Advantages and disadvantages of lazy learning algorithms

Classification using Soft-Computing     Download PPT Slides       

1. Introduction to Soft Computing
2. Introduction to Rough Set Theory
3. Reduct Computation Techniques
4. Classification using Rough Set Theory            
5. Using Rosetta Tool for Reduct computation and data Classification          
6. Major Issues in Rough Set Theory for Data Mining
7. Fuzzy Set and Data Mining      Download PPT Slides

Cluster Analysis                                        Download PPT Slides           Download PPT More Slides

1. What Is Cluster Analysis? 
2. Types of Data in Cluster Analysis 
3. A Categorization of Major Clustering Methods

Mining Spatial, Multimedia, Text, and Web Data             

1. Spatial Data Mining 
2. Multimedia Data Mining 
3. Text Mining                                                 Download PPT Slides 
4. Mining the World Wide Web                         Download PPT Slides

Applications and Trends in Data Mining            

1. Data Mining Applications 
2. Data Mining System Products and Research Prototypes 
3. Additional Themes on Data Mining 
4. Social Impacts of Data Mining 
5. Data Mining Methodologies                Download PPT Slides

Data Warehouse and OLAP Technology: An Overview  Download PPT Slides

1. What Is a Data Warehouse? 
2. A Multidimensional Data Model 
3. Data Warehouse Architecture 
4. Data Warehouse Implementation 
5. From Data Warehousing to Data Mining