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Course number: PO6017 Required course or Elective course: Required Course hours: Credits: 48 3 Type of Assessment: In-class quiz 20% Assignment 40% Group project report and presentation 40% Prerequisites / Corequisites: None Teacher: General course objectives / course profile This course aims to: Course title: Data Mining Schedule: Spring semester Form of course: Lectures Language of instruction English It is an introduction to the field of data mining (also known as knowledge discovery from data, or KDD for short). It focuses on fundamental data mining concepts and techniques for discovering interesting patterns from data in various applications It emphasizes techniques for developing effective, efficient, and scalable data mining tools. Learning outcomes: After completion of this course the student should be able to: • Understand what Is Data Mining, what kinds of data can be mined, what kinds of patterns can be mined, and what kinds of applications are targeted. • Explain major Issues in data mining. • Applymachine learning, pattern recognition, statistics, visualization, algorithm, database technology and high-performance computing in data mining applications. • Identify what kinds of technologies are used for different application. • Manipulate data preprocessing, data Warehouse and OLAP technology, data cube technology; mining frequent patterns andassociation, classification, clustering, and outlier detection. Content: Getting to Know Your Data; Data Preprocessing; Data Warehouse and OLAP Technology: An Introduction; Advanced Data Cube Technology; Mining Frequent Patterns & Association: Basic Concepts; Mining Frequent Patterns & Association: Advanced Methods; Classification: Basic Concepts ; Classification: Advanced Methods; Cluster Analysis: Basic Concepts; Cluster Analysis: Advanced Methods; Outlier Analysis. Teaching materials and reference books: Data Mining: Concepts and Techniques (Third Edition).Jiawei Han, MichelineKamber, Jian Pei. Morgan Kaufmann, 2012.