The Course will cover the following materials: Knowledge discovery fundamentals, data mining concepts and functions, data pre-processing, data reduction, mining association rules in large databases,classification and prediction techniques, clustering analysis algorithms, data visualization, mining complex types of data (text mining, multimedia mining, Web mining … etc), data mining languages, datamining applications and new trends.
Text Book :
Data Mining: Concepts and Techniques, 2nd Ed., Jiawei Han and Micheline Kamber, Morgan Kaufmann, 2006. ISBN 1-55860-901-6 Book Web site: http://www-faculty.cs.uiuc.edu/~hanj/bk2/index.html
Note : From this Website, students can download the Original Book Slides prepared by the Authors of the Book.
Course Objectives:
- Introduce students to the basic concepts and techniques of Data Mining.
- Develop skills of using recent data mining software for solving practical problems.
Learning Outcomes:
After completing this course, the student should demonstrate the knowledge and ability to: · Explain the importance of Data Mining as a new discipline in the IT field. · Show and understand the various kinds of Data Mining Tasks. · Use data mining to solve real life problems. · Using some of open source data mining tools.
Course Outline Get the PDF version of the Course Syllabus
Chapter 1: Introduction Get Slides in PDF
1.1 What Motivated Data Mining? Why Is It Important? 1.2 So, What Is Data Mining? 1.3 Data Mining--On What Kind of Data? 1.4 Data Mining Functionalities—What Kinds of Patterns Can Be Mined? 1.5 Are All of the Patterns Interesting? 1.6 Classification of Data Mining Systems 1.7 Data Mining Task Primitives 1.8 Integration of a Data Mining System with a Database or Data Warehouse System 1.9 Major Issues in Data Mining
Chapter 2: Data Preprocessing Get Slides in PDF
2.1 Why Preprocess the Data? 2.2 Descriptive Data Summarization 2.3 Data Cleaning 2.4 Data Integration and Transformation 2.5 Data Reduction 2.6 Data Discretization and Concept Hierarchy Generation
Chapter 5: Mining Frequent Patterns and Associations Get New Slides in PDF
5.1 Basic Concepts and a Road Map 5.2 Efficient and Scalable Frequent Item set Mining Methods 5.3 Mining Various Kinds of Association Rules
Chapter 6: Classification and Prediction (Chp 6) Get New Slides in PDF Get Math Pages File
6.1 What Is Classification? What Is Prediction? 6.2 Issues Regarding Classification and Prediction 6.3 Classification by Decision Tree Induction Get Slides 6.4 Bayesian Classification Get Slides 6.5 Rule-Based Classification Get Slides 6.6 KNN Classifier Get Slides 6.11 Prediction 6.12 Accuracy and Error Measures 6.13 Evaluating the Accuracy of a Classifier or Predictor
Chapter 7 : Cluster Analysis Get Slides in PDF
7.1 What Is Cluster Analysis? 7.2 Types of Data in Cluster Analysis 7.3 A Categorization of Major Clustering Methods
Chapter 10 : Mining Spatial, Multimedia, Text, and Web Data Get Slides in PDF
10.2 Spatial Data Mining 10.3 Multimedia Data Mining 10.4 Text Mining 10.5 Mining the World Wide Web
Chapter 11: Applications and Trends in Data Mining Get Slides in PDF
11.1 Data Mining Applications 11.2 Data Mining System Products and Research Prototypes 11.3 Additional Themes on Data Mining 11.4 Social Impacts of Data Mining 11.5 Trends in Data Mining
Required Software
WEKA is a software for machine learning and data mining . WEKA is an open source software issued under the GNU General Public License. Download the software from: http://www.cs.waikato.ac.nz/ml/weka/
See the Software page for other Recourses (Software and Datasets).
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