CIS 453/553, Data Mining

CIS453/553, Spring 2018

Data Mining

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Course Description: Data Mining is a multidisciplinary field which involves databases, machine learning, statistics, knowledge acquisition, information retrieval, high-performance computing, and data visualization. Data Mining emerged as an interesting research topic since 1980s and has made great strides during the 1990s. It also has many successful real world applications in the age of Big Data. In this course, we will study methods for data management and preprocessing, data warehousing, concept description, association rule mining, classification and prediction, deep learning, data cluster analysis and outlier analysis. We will view data mining as the discovery of interesting patterns in data that can be used to describe or summarize the data and to predict future instances of the data. Such patterns are considered to be knowledge discovered by the data mining process. Students will be guided to apply or build different mining tools by themselves to analyze real data sets, such as biomedical data, financial data, health data, network data, retail data, social data, and sports data.
Prerequisites: Database, AI, and machine learning related courses recommended (e.g., CIS 451, CIS 452, CIS 471, CIS 472) .
Time and Place: Wednesday and Friday at 2:00pm - 3:20pm in 200 Deschutes Hall.
Instructor: Dejing Dou, 303 Deschutes, phone 541-346-4572, email dou@cs.uoregon.edu.
Office hours:
- Dejing Mondays 4:00pm - 5:00pm or by appointment (I like to talk with students).
News: Keep an eye on the course news for miscellaneous announcements.
Evaluation:
- The homework will be a combination of written and programming work.
- The Final Project will be due in parts: Part 1 (project proposal) is due on Friday May 25, presentations are on Friday June 8, and Part 2 (final report) on Friday June 15. We assume that you will work on it individually. If you wish to work in a group, please check with the instructor.
The approximate weighting of the work done will be

Homework Assignments 40%

Midterm 30%

Final Project 30%
Texts:
- (Required) Jiawei Han, Micheline Kamber, and Jian Pei Data Mining: Concepts and Techniques, Third Edition, Morgan Kaufmann. (see text website.)
Supplemental Reading:
- I. Witten and E. Frank Data Mining: Practical Machine Learning Techniques with Java Implementations, Morgan-Kaufmann, 2000. (Good for the final project)

Approximate Syllabus and Lecture Slides

Week of	Chapter	Description and Slides	Events

Apr 2	1	intro, issues in Data Mining
Apr 9	2, 3	data features, data preprocessing, data warehouse
Apr 16	4, 5	data cube, design, OLAP, OLAM
Apr 23	6, 7	Association Mining, Pattern Mining
Apr 30	8	Classification and Prediction
May 7	8,9	Classification and Prediction, Deep Learning	Read Deep Learning paper
May 14	10	Clustering, Midterm Review (Friday)
May 21	11, 12	Midterm (Wednesday), Outlier Analysis, Complex Types	Midterm: Wednesday, May 23, Final Project Part 1 (proposal): Friday, May 25
May 29		Project implementations (no lectures)
June 4	13	Graph, Social Network Analysis, Project Presentations (Friday)
June 11		Project Final Report Writing;	Final Project Part 2 (final report): Friday, June 15

Late policy: All assignments are due by 11:59pm of the stated due date, though it is much safer to turn them in at class time. Late papers are subject to a 10% per day penalty. Late assignments will not be accepted once the graded papers are returned or once sample solutions are handed out. It is your responsibility to turn in the assignments on time. Make sure that electronically submitted assignments are actually delivered.
Some useful links:
- Weka data mining software.
- RuleQuest research data mining tools.
- Data Mining Research Softwares collected by Prof. Mohammed J. Zaki at RPI.
- UC Irvine Machine Learning Repository

Homework Assignments	40%
Midterm	30%
Final Project	30%