Text Mining

Dr. Nitin Indurkhya
with Dr. Fred Damerau

Aim of Course:

This course will introduce the essential techniques of text mining, understood here as the extension of data mining's standard predictive methods to unstructured text. This course will discuss these standard techniques, and will devote considerable attention to the data preparation and handling methods that are required to transform unstructured text into a form in which it can be mined.

Who Should Take This Course:

IT professionals, web marketing analysts, data mining and statistical consultants. In general: analysts and researchers who need to pilot, implement or analyze data mining methods aimed at data containing unstructured text (forms, surveys, etc.).

For those enrolled in Professional Advancement Programs, this is a required or elective course in the following Programs:

• Data Mining - elective

Course Program:

The course is structured as follows

The Instructor:

The lead instructor is Nitin Indurkhya, co-author of Text Mining, and professor at the School of Computer Science and Engineering, University of New South Wales, Sydney, Australia. He is also the founder and president of Data-Miner Pty Ltd, an Australian company engaged in data-mining consulting and education. He has published extensively on data-mining and has considerable experience with industrial data-mining applications in many countries such as Australia, Japan and the United States. He will be assisted by Dr. Fred Damerau. Dr. Fred Damerau, prior to his retirement, was a researcher at the Thomas J. Watson Research Center, Research Staff Linguistics group, where he worked on machine learning approaches to natural language processing. He is a co-author (With Weiss, Indurkhya and Zhang) of Text Mining (the course text).

Organization of the Course:

The course takes place over the internet, at statistics.com. During each course week, you participate at times of your own choosing - there are no set times when you must be online. Course participants will be given access to a private discussion board. In class discussions led by the instructor, you can post questions, seek clarification, and interact with your fellow students and the instructor. The course is scheduled to take place over 4 weeks, and typically requires 10-15 hours per week. At the beginning of each week, you receive the relevant material, in addition to answers to exercises from the previous session. During the week, you are expected to go over the course materials and work through exercises. Discussion among participants is encouraged. The instructor will provide answers and comments.

Certificates and Grades:

You may be interested only in learning the material presented, and not be concerned with grades or certificates. Or you may be enrolled in a statistics.com Professional Advancement Program that requires demonstration of proficiency in the subject, in which case your work will be assessed for purposes of issuing a grade. Or you may require only a "Certificate of Course Completion," along with professional development credit in the form of Continuing Education Units (CEU's). As you begin the class, you will be asked to specify your category.

Credit:

This course offers continuing education units (CEU's). For those successfully completing the course (generally this means marks of 50% or better on the homework), 5.0 CEU's and a certificate will be issued by statistics.com, upon request.

Dates:

Jun. 20 - Jul. 18, 2008
Click here to be notified of future course offerings.

Participants gain access to the online materials on the first day of the course, and typically spend about 10-15 hours per week (at their convenience). You retain full access to course materials, including discussion board, for two weeks after the course closing date.

Level:

Intermediate

Prerequisite:

The equivalent of Introduction to Statistics I: Inference for a Single Variable, and Introduction to Statistics II: Working with Bivariate Data (and, if necessary before these courses, Introduction to Statistics for Beginners or Survey of Statistics for Beginners). Math beyond algebra is not required to learn what text mining methods do, and how they can be used, though there is some detail on algorithms that employs more advanced math, for those interested in pursuing it. You should also have some familiarity with standard data mining supervised learning methods, such as those covered in Introduction to Data Mining. Also, you should be comfortable with learning the software used in this course (see below). For additional information about course prerequisites, click here.

Course Text:

The required text is Text Mining, by Weiss, Indurkhya, Zhang and Damerau. You may order this text directly from the publisher at a discounted price by clicking here and using the promotional code, AECT15 (this code is case-sensitive), during checkout time. PLEASE ORDER YOUR COPY IN TIME FOR THE COURSE STARTING DATE.

Note: Springer prints this text "on demand," which adds 1-2 weeks to delivery time. If it is within 3 weeks of the course date, please order your book by phone (212-460-1500) and specify "rush".

Software:

The software that is needed (TMSK and RIKTEXT) is provided in conjunction with the text. These software programs are Java-based and run on Linux, and also from the Windows command line shell. You should be comfortable running software from a command line, and should familiarize yourself with these programs by downloading and running them from the web site below. The software can be downloaded from http://www.data-miner.com with the username and password provided in the textbook's appendix.

Registration:

Register Online - $449
Register Online (academic) - $349 (you must be affiliated with a college, university or high school)

Add $50 service fee if you require a prior invoice, or if you need to submit a purchase order or voucher, pay by wire transfer or EFT, or refund and reprocess a prior payment. Please use this printed registration form, for these and other special orders.

Note: Courses may fill up at any time and registrations are processed in the order in which they are received. Your registration will be confirmed for the first available course date, unless you specify otherwise.