Introduction to Data Mining

Course Description:

This course covers concepts and techniques in the field of data mining. This includes both supervised and unsupervised algorithms. Various issues in the pre-processing of the data are addressed. The students learn the material by building various data mining models and using various data preprocessing techniques, performing experimentations and provide analysis of the results.

Prerequisite:

Data Structures and comfortable programming knowledge!

Textbook:

You should have one of these textbooks-- your choice to choose! Both are very good books:

• Jiawei Han and Micheline Kamber, Data Mining Concepts and Techniques, Morgan Kaufmann
• Pang-Ning Tan, Michael Steinbach, Vipin Kumar, Introduction to Data Mining, Addison Wesley

Teaching Assistant:

TBD

Office hours: see Blackboard.

Grading & Due Dates (Tentative- will be finalized by the 1st day of the class):

Projects (Individual)		3-4 projects -- The outcome of each project is a data mining engine .
(Only in some semesters)Research Paper Presentation
Exams (2-3 exams - will be announced in the class)

Tentative Course Outline:

Introduction to Data Mining: Knowledge Discovery, Data Warehousing, Data Mining
Data preprocessing
Intro to Classification
Evaluation
Naive Bayes
Neural Networks
Decision Tree
Rule Based Classification
K-Nearest Neighbor
Support Vector Machine
Ensemble Methods
Association rules
Cluster analysis
Text Categorization
Recommander Systems

Late Assignment Policy:

Will be posted on the class syllabus by the first week of each semester.

Academic Integrity:

Visit the Honor System Website at http://gervaseprograms.georgetown.edu/honor/