Text Mining

Brief Description:

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.

Instructor(s):
Level: Intermediate

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.).

Dates:
June 08, 2012 to July 06, 2012
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Registration:
Please read the syllabus tab, noting the prerequisites, text and software requirements.

Register Online -$499
Register Online -$399 (you must be affiliated with a college, university or high school)

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Text Mining

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.

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

• Data Mining

Prerequisite(s):

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 have some familiarity with standard data mining supervised learning methods, such as those covered in Introduction to Data Mining and you should be comfortable with learning the software used in this course (see below).

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

Organization of the Course:

This course takes place over the internet, at statistics.com 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 you will receive individual feedback on your homework answers.

Credit:
Students come to The Institute for a variety of reasons:

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 (Program in Advanced 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).

As you begin the class, you will be asked to specify your category.

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 record of course completion will be issued by Statistics.com, 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:

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 once you install the software.

Register Now

Yes, I want to register for:

Text Mining

Instructor(s):
Dates:
June 08, 2012 to July 06, 2012
Course Fee: $499
Academic Discounted Rate: $399

Before registering, please read the syllabus tab, noting the prerequisites, text and software requirements. When you click the register button, you will be taken to our secure transaction page.

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