King Saud University

Transcription

1 King Saud University College of Computer and Information Sciences Department of Computer Science CSC 493 Selected Topics in Computer Science (3-0-1) - Elective Course CECS 493 Selected Topics: DATA MINING Instructor: Dr. Mohamed Maher Ben Ismail Phone: (966) mbenismail@ksu.edu.sa Office hours: Monday/Wednesday 9:00 am. 11:00 am. Lectures: Sunday/Tuesday 8:00 am - 10:00 a.m. Course Description: This course will introduce concepts, models, methods, and techniques of data mining, including regression, rule association, and decision trees. Some software tools and successful real world datamining applications will also be introduced. Course Objectives: After taking this course, the student should: - Understand the basics of data mining process, and requirements in its every phase to build a successful application. - Understand the basic data-mining techniques and will be able to use standard, or to develop new software tools for data mining. Textbook: Mehmed Kantardzic, Data Mining: Concepts, Models, Methods, and Algorithms, November 2002, Wiley-IEEE Press. Recommended References: Tan P., Steinbach M., Kumar V., Introduction to Data Mining, Addison-Wesley, Boston, MA, 2006.

2 Grading Policy: Final exam and course project are elements for final grading. Course projects are practical application of standard data mining tools on the experimental data sets (both are available on the Internet), or implementation of these tools in one of the standard programming languages. Final report on the projects is required. The passing cumulative score is 60%. Homework 10 % Quiz 10 % Mid-Term1 15% Mid-Term2 15% Project 10 % Final 40% ===== Total 100 % Grading Scale: A 90% & up B 80% % C 70% % D 60% % F below 60% Course Objectives: The objective of this course is to develop the students' ability to: - Understand the basics of data mining process, and requirements in its every phase to build a successful application. - Understand the basic data-mining techniques and will be able to use standard, or to develop new software tools for data mining. - Ameliorate the students research and writing skills.

3 Course Outcomes: Students are expected to be able to: 1. Understand the different steps of data mining process 2. Hands-on pre-processing real data sets. 3. Design skills using data mining tool (weka) 4. Elaborate a detailed plan of a scientific research or technical investigation project 5. Write scientific and technical reports 6. Work in teams to design, implement, and test programs Topics: The following is the tentative schedule of topics, which will be covered during the classes. 1. Data mining concepts and data mining process. 2. Preparing the data: Missing data, Similarity measures, and data Normalization. 3. Data reduction: features reduction, values reduction, and cases reduction. 4. Decision trees and decision rules: C4.5 algorithm. 5. Instance based classifier: K Nearest Neighbors. 6. Probability based classifier: Naïve Bayes Classifier. 7. Association rules. Market basket analysis and Apriori algorithm. 8. Statistical methods in data mining. Linear regression and Non-Linear regression. 9. Cluster analysis: Agglomerative and partitional clustering. Relationship of Course to ABET Criteria: Criterion 2 - Program Educational Objectives: This course allows the student to gain the necessary skills to lead, design, develop, and maintain computer-related projects in various fields (PEO 1).

4 Criterion 3 - Program Outcomes: a. an ability to apply knowledge of mathematics, computing, science, and engineering appropriate to the discipline Students pre-process real data sets and run data mining algorithms manually and using data mining tools (weka) b. An ability to analyze a problem, and identify and define the computing requirements appropriate to its solution; c. An ability to design, implement and evaluate a computer-based system, process, component, or program to meet desired needs; Students learn about various real-world applications and desing data mining processes using several algorithms to propose solutions. d. An ability to function effectively on teams to accomplish a common goal; Students work in teams to realize a project. e. An understanding of professional, ethical, legal, security, and social issues and responsibilities; f. An ability to communicate effectively with a range of audiences; Students learn how to write scientific reports g. An ability to analyze the local and global impact of computing on individuals, organizations and society; h. Recognition of the need for, and an ability to engage in, continuing professional development; Students learn how to apply appropriate data mining technique to solve problems i. An ability to use current techniques, skills, and tools necessary for computing practices. j. An ability to apply mathematical foundations, algorithmic principles, and computer science theory in the modeling and design of computer-based systems in a way that demonstrates comprehension of the tradeoffs involved in design choices; k. An ability to apply design and development principles in the construction of software systems of varying complexity.

5 Course outcomes Program outcomes (a) (c) (d) (f) (h) 40% 20% 10% 10% 20% 1 X 2 X X X 3 X X X 4 X X X 5 X 6 X X X Criterion 4 Professional Component: The course prepares students to be effective data miner and pattern recognition scientist. Prepared by: Mohamed Maher Ben Ismail, Dec 2011 Reviewed by: Revised by: Approved by: