COMP7103B - Data mining

Semester 2, 2020-21

Instructor	Professor Mauro Sozio
Teaching assistant	Mr. Zhiyong Wu
Syllabus	Data mining is the automatic discovery of statistically interesting and potentially useful patterns from large amounts of data. The goal of the course is to study the main methods used today for data mining and on-line analytical processing. Topics include Data Mining Architecture; Data Preprocessing; Mining Association Rules; Classification; Clustering; On-Line Analytical Processing (OLAP); Data Mining Systems and Languages; Advanced Data Mining (Web, Spatial, and Temporal data).
Introduction by Instructor	Advances in data collection and generation technologies are producing massive amounts of data from which valuable information and knowledge can be derived. In this course we study various data mining techniques, which are powerful tools for data analysts to process data and to extract from it interesting patterns and models. These models allow new scientific discoveries and intelligent business decisions be made.
Learning Outcomes	Course Learning Outcomes Relevant Programme Learning Outcome CLO1. Understand the knowledge discovery process, which includes data collection, data cleaning, model building, model testing and evaluation. PLO.6, 7, 8, 9, 10, 11, 12 CLO2. Understand the various data mining tasks and the principle algorithms for addressing those tasks. PLO.5, 6, 7, 8, 9, 16 View Programme Learning Outcomes
Pre-requisites	Nil
Compatibility	Nil
Topics covered	Course Content No. of Hours Course Learning Outcomes 1. Data Cleaning 4 CLO1 2. Data Exploration 4 CLO1 3. Ranking 4 CLO1 4. Clustering 6 CLO2 5. Association Rules 4 CLO2 6. Recommender Systems 3 CLO2 7. Advanced applications 5 CLO1, CLO2
Assessment	Description Type Weighting * Examination Period ^ Course Learning Outcomes Lab Session Continuous Assessment 25% - CLO1, CLO2 Lab Session Continuous Assessment 25% - CLO1, CLO2 Written exam Written Examination 50% May 10 to May 29, 2021 CLO1, CLO2 * The weighting of coursework and examination marks is subject to approval ^ The exact examination date uses to be released when all enrolments are confirmed after add/drop period by the Examinations Office.  Students must oblige to the examination schedule. Students should NOT enrol in the course if they are not certain that they will be in Hong Kong during the examination period.  Absent from examination may result in failure in the course. There is no supplementary examination for all MSc curriculums in the Faculty of Engineering.
Course materials	Prescribed textbook: Introduction to Data Mining, by Tan, Steinbach, and Kumar, Addison Wesley, 2006. Mining of Massive Datasets, J. Leskovec, A. Rajaraman, J. D. Ullman, Cambridge 2014 (Optional).
Session dates	Date Time Venue Remark Session 1 28 Jan 2021 (Thu) 7:00pm - 10:00pm Online Zoom Session 2 4 Feb 2021 (Thu) 7:00pm - 10:00pm Online Zoom Session 3 25 Feb 2021 (Thu) 7:00pm - 10:00pm Online Zoom Session 4 4 Mar 2021 (Thu) 7:00pm - 10:00pm Online Zoom Session 5 18 Mar 2021 (Thu) 7:00pm - 10:00pm Online Zoom Session 6 25 Mar 2021 (Thu) 7:00pm - 10:00pm Online Zoom Session 7 1 Apr 2021 (Thu) 7:00pm - 10:00pm Online Zoom Session 8 8 Apr 2021 (Thu) 7:00pm - 10:00pm Online Zoom Session 9 15 Apr 2021 (Thu) 7:00pm - 10:00pm Online Zoom Session 10 22 Apr 2021 (Thu) 7:00pm - 10:00pm Online Zoom
Add/drop	18 January, 2021 - 4 February, 2021
Quota	110   [For Engineering TPG students]