Princess Sumaya University for Technology
11446 - Special Topics in Computer Science II
Introduction to Data Mining - Fall 2013
Time: Mo, We 11:00 - 12:30
Room: IT-101
Instructor Information
Dr. Ibrahim Albluwi
Email: i.albluwi@psut.edu.jo
Office: IT-309
Extension: 243
Extension: 243
Office Hours: SuTuThu: 11:00-12:30, Monday 12:30-14:00
Course Description
Recent years have witnessed an exponential growth in the amount of digital data in almost all fields. This has led to the development of a wide range of techniques that can help in making sense out the available data and in producing sound judgements and decisions based on them.This course is an introduction to the basic challenges and techniques in the field of Data Mining, which is a field that is concerned with the interpretation of large datasets to find patterns, relations and other interesting pieces of information.
After completing this course, students will be able to:
- Explain what data mining is, why it is important and which problems can be tackled by its techniques.
- Describe the main challenges and techniques of the data preparation phase.
- Use and compare between at least three classification techniques (For e.g. Decision Trees, Bayesian Networks and KNN).
- Use and compare between at least two clustering techniques (For e.g. K-Means and DBSCAN).
- Use at least one Association Mining technique.
- Use at least one data mining (and/or) data exploration tool.
Prerequisites
- Data Structures and Introduction to Algorithms (CS212).- Basic knowledge of statistics and database management systems.
- Good programming skills in at least one programming language.
Textbook
All required material will be provided as either links to online resources or as photocopied excerpts from books. However, most of the material will be based on the following book:
Introduction to Data Mining,
Pang-Ning Tan,
Michael Steinbach,
Vipin Kumar,
First Edition, Addison-Wesley.
http://www-users.cs.umn.edu/~kumar/dmbook/index.php
- Jiawei Han, Micheline Kamber, Jian Pei, Data Mining: Concepts and Techniques, Third Edition (The Morgan Kaufmann Series in Data Management Systems).
- Ian H. Witten, Eibe Frank, Mark A. Hall, Data Mining: Practical Machine Learning Tools and Techniques, Third Edition (The Morgan Kaufmann Series in Data Management Systems).
Grades Distribution
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