CSCI-4390/6390 Data Mining
Fall 2008

Introduction
With the unprecedented rate at which data is being collected today in almost all fields of human endeavor, there is an emerging economic and scientific need to extract useful information from it. Data mining is the process of automatic discovery of patterns, changes, associations and anomalies in massive databases. This course will provide an introduction to the main topics in data mining and knowledge discovery, including: statistical foundations, association discovery, classification, clustering, database support, and so on. Emphasis will be laid on the algorithmic and systems issues, as well as application of mining in real-world problems.

Textbook
There is no required text for the course. Notes will be handed out in class. Class notes from previous years are available by following the links on the course web page. For example, the notes from last year are available at: http://www.cs.rpi.edu/%7Ezaki/dmcourse/fall07/notes/ .

The following text books are also good references:

• Introduction to Data Mining, by Pang-Ning Tan, Michael Steinbach, and Vipin Kumar, Addison Wesley, 2006.
• Data Mining: Concepts and Techniques (2nd edition), by Jiawei Han and Micheline Kamber, Morgan Kaufmann, 2006.

Grading Policy
The pre-requisites for this course include data structures and algorithms. Basics of linear algebra, and probability & statistics will be very useful as well.

Your grade will be a combination of the following items:

• Quizzes (20%): There will be a quiz at the beginning of (nearly) each class, covering the material from the previous lecture (lowest 2-3 grades will be dropped). Previous years quizzes can be accessed at: http://www.cs.rpi.edu/~zaki/dmcourse/fall07/quiz/.
• Assignments (20%): The assignments are meant to be practically oritented. You'll be asked to run some mining methods on some real datasets, etc., to complement the theory.
• Exams (60%): There will be three exams covering the main topics of the course. The tentative exam schedule is posted on the class schedule page. There is no comprehensive final exam.

Attendance: Students are strongly encouraged to participate in the class, and should try to attend all classes, unless there are exiating circumstances. Students are also encouraged to point out any typograpical or grammatical errors in the notes. Also feel free to point out parts of the notes that are not clear or may be improved by adding additional description or examples.

Academic Integrity

You may consult other members of the class on the homeworks, but you must submit your own work. Anytime you borrow material from the web or elsewhere, you must acknowledge the source.

The school takes cases of academic dishonesty very seriously, resulting in an automatic "F" grade for the course. Students should familiarize themselves with the relevant portion of the Rensselaer Handbook of Student Rights and Responsibilities on this topic.