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HW1 posted. Due date 3/4.

Project guidelines posted.

HW2 posted. Due date 4/8.

4/9: HW3 posted. Due dates are 4/15 and 4/22. Here are the datasets.

See the new final exam schedule below.

Course Description

Basic principles and methods for data analysis and knowledge discovery. Emphasizes developing basic skills for modeling and prediction, on one side, and performance evaluation, on the other. Topics include system design; data quality, preprocessing, and association; event classification; clustering; biometrics; business intelligence; and mining complex types of data.

Instructor:

Dr. Jessica Lin

Office: Engineering Building 4419
Phone: 703-993-4693
Email: jessica [AT] cs [DOT] gmu [DOT] edu
Office Hours: Thursday 2-4pm

Tanwistha Saha

Office: Engineering Building 4456
Office Hours: Monday 3-5pm
Email: tsaha [AT] cs [DOT] gmu [DOT] edu

Classes

Monday/Wednesday
1:30-2:45pm
Innovation Hall 206

Course Outcomes

The ability to apply computing principles, probability and statistics relevant to the data mining discipline to analyze data.
A thorough understanding of model programming with data mining tools, algorithms for estimation, prediction, and pattern discovery.
The ability to analyze a problem, identifying and defining the computing requirements appropriate to its solution: data collection and preparation, functional requirements, selection of models and prediction algorithms, software, and performance evaluation.
The ability to understand performance metrics used in the data mining field to interpret the results of applying an algorithm or model, to compare methods and to reach conclusions about data.
The ability to communicate effectively to an audience the steps and results followed in solving a data mining problem (through a term project)

Prerequisites:

Grade of C or better in CS 310 and STAT 344

Grading

Assignments: 15%
Class Participation: 5%
Project: 30%
Midterm: 20%
Final: 30%

Exams

Exams will be open-book, open-note. Prior arrangement needs to be made with the instructor if you cannot make it to the exam. Missed exams cannot be made up.

Honor Code Statement

Please be familiar with the GMU Honor Code. In addition, the CS department has its own Honor Code policies. Any deviation from this is considered an Honor Code violation.

Textbooks

Required: Introduction to Data Mining by Pang-Ning Tan, Michael Steinbach, and Vipin Kumar

Additional handouts and reading materials may be given in class.

Topics

Ch.1: Introduction
Ch.2: Data
Ch.4: Classification
Ch.5: Classification: Alternative Techniques
Ch.6: Association Analysis: Basic Concepts and Algorithms
Ch.7: Association Analysis: Advanced Concepts
Ch.8: Cluster Analysis: Basic Concepts and Algorithms
Ch.9: Cluster Analysis: Additional Issues and Algorithms
Ch.10: Anomaly Detection

Tentative Schedule

No	Dates	Topics	Notes
1	1/21 1/23	No class Introduction 1 (Ch. 1)
2	1/28 1/30	Introduction 2 Data 1
3	2/4 2/6	Data 2 Data 3
4	2/11 2/13	Data 4 Classification 1 & 2	HW1 posted
5	2/18 2/20	Classification 3 Classification 4
6	2/25 2/27	Classification 5 Classification 6	Project posted
7	3/4 3/6	Classification 7 Snow Day
8	3/11 3/13	Spring Break
9	3/18 3/20	Clustering 1 Midterm	HW1 & Project Proposal due (3/18) HW2 posted
10	3/25 3/27	Clustering 2 Post-midterm review/Clustering 3
11	4/1 4/3	Clustering 4 Association Analysis 1	HW3 posted
12	4/8 4/10	Association Analysis 2 Association Analysis 2, con't	HW2 due
13	4/15 4/17	Association Analysis 3	HW3 Part 1 due
14	4/22 4/24	Anomaly Detection Recommendation Systems	HW3 Part 2 due
15	4/29 5/1	Review Final Exam
16	5/6 5/8	No class Presentations (1:30-4:15pm)