Text Mining

Instructor(s):

Dates:

June 07, 2013 to July 05, 2013 June 06, 2014 to July 04, 2014

Text Mining

taught by Nitin Indurkhya and Luis Torgo

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.

Sofware:  R may be used, or the TMSK and RIKTEXT programs that come with the text.  See the software section found under the "Requirements" tab.

This course is a core requirement or elective in the following Program(s) in Analytics and Statistical Studies (PASS):

Data Analytics

Course Program:

SESSION 1: Introduction and Data Preparation

• Overview of text mining
• Tokenization
• Dictionary creation
• Vector generation for prediction
• Feature generation and selection
• Parsing

SESSION 2: Predictive Models for Text

• Document classification
• Document similarity and nearest-neighbor
• Decision rules
• Probabilistic models
• Linear models
• Performance evaluation
• Applications

SESSION 3: Retrieval and Clustering of Documents

• Measuring similarity for retrieval
• Web-based document search and link analysis
• Document matching
• Clustering by similarity
• k-means clustering
• Hierarchical clustering
• The EM algorithm for clustering
• Evaluation of clustering

SESSION 4: Information Extraction

• Goals of information extraction
• Finding patterns and entities
• Entity Extraction: The Maximum Entropy method
• Template filling
• Applications

HOMEWORK:

Homework in this course consists of short answer questions to test concepts and guided data analysis problems using software.

Text Mining

Instructor(s):

Dates:
June 07, 2013 to July 05, 2013 June 06, 2014 to July 04, 2014
Course Fee: $499

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.

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Have you reviewed the REQUIREMENTS for this course?

Text Mining

taught by Nitin Indurkhya and Luis Torgo

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.).

Level:

Intermediate

Organization of the Course:

This course takes place online at the Institute for 4 weeks. 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 typically requires 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, work through exercises, and submit answers. Discussion among participants is encouraged. The instructor will provide answers and comments, and at the end of the week, you will receive individual feedback on your homework answers.

Credit:
Students come to the Institute for a variety of reasons. As you begin the course, you will be asked to specify your category:

1. You may be interested only in learning the material presented, and not be concerned with grades or a record of completion.
2. You may be enrolled in PASS (Programs in Analytics and Statistical Studies) that requires demonstration of proficiency in the subject, in which case your work will be assessed for a grade.
3. You may require a "Record of Course Completion," along with professional development credit in the form of Continuing Education Units (CEU's).  For those successfully completing the course, 5.0 CEU's and a record of course completion will be issued by The Institute, upon request.

Course Text:

The required text is Fundamentals of Predictive Text Mining by Weiss, Indurkhya and Zhang. 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.

Software:

There are two options:

(1) R:  You should be familiar with R and able to run statistical procedures in it and/or manipulate data in it.

(2) The TMSK and RIKTEXT programs that are 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 them, then installing and running them.