Advanced Data Mining

Advanced Data Mining

The Course will cover the following materials:

a) 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 mining languages, data mining applications and new trends.
b) Advanced Knowledge discovery in semi-structured/unstructured data repositories with emphasis on emerging computational intelligence paradigms such as soft computing and artificial life. Application will be visited in special themes: advanced transactionaldata mining, Web Mining, Text Mining, Bioinformatics, and other scientific and engineering applications.
Text Book :

Data Mining: Concepts and Techniques, 1st or 2nd Ed., Jiawei Han and Micheline Kamber, Morgan Kaufmann, 2003 or 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.Get the New Slide which Includes Students Presentations.

Course Outline:

Get the PDF version of the Course Syllabus
Introduction Get 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 Get SlidesGet 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 Get 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 Get Slides

1 What Is Classification? What Is Prediction?
2 Issues Regarding Classification and Prediction
3 Classification by Decision Tree Induction Get More Slides
4 Bayesian Classification Get Slides
5 Rule-Based Classification Get 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 Get Slides

Classification Using Lazy Learning Techniques Get 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 Get 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 Get Slides

Cluster Analysis Get Slides Get 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 Get Slides

1 Spatial Data Mining
2 Multimedia Data Mining
3 Text Mining Get Slides
4 Mining the World Wide Web Get 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 Get Slides

Data Warehouse and OLAP Technology: An Overview Get 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

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/

Rosetta is a software for data reduction and classification purposes based on the concepts of Rough Set Theory.
Download the software from: http://rosetta.lcb.uu.se/general/

See the Software pagefor other Recourses (Software and Datase)