Cs583 – Data Mining and Text Mining Course Web Page

CS583 – Data Mining and Text Mining

• Course Web Page

• http://www.cs.uic.edu/~liub/teach/cs583-fall-05/cs583.html

General Information

• Instructor: Bing Liu

• Email: [e-mail]
• Tel: [phone]
• Office: SEO 931

• Course Call Number: 22887

• Lecture times:

• 11:00am-12:15pm, Tuesday and Thursday

• Room: 319 SH

• Office hours: 2:00pm-3:30pm, Tuesday & Thursday (or by appointment)

Course structure

• The course has three parts:

• Lectures - Introduction to the main topics
• Programming projects
• 2 programming assignments.
• To be demonstrated to me
• Research paper reading
• A list of papers will be given

• Lecture slides will be made available at the course web page

Programming projects

• Two programming projects

• To be done individually by each student

• You will demonstrate your programs to me to show that they work

• You will be given a sample dataset

• The data to be used in the demo will be different from the sample data

Grading

• Final Exam: 50%

• Midterm: 30%

• 1 midterm

• Programming projects: 20%

• 2 programming assignments.

• Research paper reading (some questions from the papers will appear in the final exam).

Prerequisites

• Knowledge of

• basic probability theory
• algorithms

Teaching materials

• Text

• Reading materials will be provided before the class

• Reference texts:

• Data mining: Concepts and Techniques, by Jiawei Han and Micheline Kamber, Morgan Kaufmann, ISBN 1-55860-489-8.
• Principles of Data Mining, by David Hand, Heikki Mannila, Padhraic Smyth, The MIT Press, ISBN 0-262-08290-X.
• Introduction to Data Mining, by Pang-Ning Tan, Michael Steinbach, and Vipin Kumar, Pearson/Addison Wesley, ISBN 0-321-32136-7.
• Machine Learning, by Tom M. Mitchell, McGraw-Hill, ISBN 0-07-042807-7
• Modern Information Retrieval, by Ricardo Baeza-Yates and Berthier Ribeiro-Neto, Addison Wesley, ISBN 0-201-39829-X

• Data mining resource site: KDnuggets Directory

Topics

• Introduction

• Data pre-processing

• Association rule mining

• Classification (supervised learning)

• Clustering (unsupervised learning)

• Post-processing of data mining results

• Text mining

• Partial/Semi-supervised learning

• Introduction to Web mining

Any questions and suggestions?

• Your feedback is most welcome!

• I need it to adapt the course to your needs.

• Share your questions and concerns with the class – very likely others may have the same.

• No pain no gain – no magic

• The more you put in, the more you get
• Your grades are proportional to your efforts.

Rules and Policies

• Statute of limitations: No grading questions or complaints, no matter how justified, will be listened to one week after the item in question has been returned.

• Cheating: Cheating will not be tolerated. All work you submitted must be entirely your own. Any suspicious similarities between students' work will be recorded and brought to the attention of the Dean. The MINIMUM penalty for any student found cheating will be to receive a 0 for the item in question, and dropping your final course grade one letter. The MAXIMUM penalty will be expulsion from the University.

• Late assignments: Late assignments will not, in general, be accepted. They will never be accepted if the student has not made special arrangements with me at least one day before the assignment is due. If a late assignment is accepted it is subject to a reduction in score as a late penalty.

Introduction to Data Mining

What is data mining?

• Data mining is also called knowledge discovery and data mining (KDD)

• Data mining is

• extraction of useful patterns from data sources, e.g., databases, texts, web, image.

• Patterns must be:

• valid, novel, potentially useful, understandable

Example of discovered patterns

• Association rules:

• “80% of customers who buy cheese and milk also buy bread, and 5% of customers buy all of them together”
• Cheese, Milk Bread [sup =5%, confid=80%]

Main data mining tasks

• Classification:

• mining patterns that can classify future data into known classes.

• Association rule mining

• mining any rule of the form X  Y, where X and Y are sets of data items.

• Clustering

• identifying a set of similarity groups in the data

Main data mining tasks (cont …)

• Sequential pattern mining:

• A sequential rule: A B, says that event A will be immediately followed by event B with a certain confidence

• Deviation detection:

• discovering the most significant changes in data

• Data visualization: using graphical methods to show patterns in data.

Why is data mining important?

• Rapid computerization of businesses produce huge amount of data

• How to make best use of data?

• A growing realization: knowledge discovered from data can be used for competitive advantage.

Why is data mining necessary?

• Make use of your data assets

• There is a big gap from stored data to knowledge; and the transition won’t occur automatically.

• Many interesting things you want to find cannot be found using database queries

• “find me people likely to buy my products”
• “Who are likely to respond to my promotion”

Why data mining now?

• The data is abundant.

• The data is being warehoused.

• The computing power is affordable.

• The competitive pressure is strong.

• Data mining tools have become available

Related fields

• Data mining is an emerging multi-disciplinary field:

• Statistics
• Machine learning
• Databases
• Information retrieval
• Visualization
• etc.

Data mining (KDD) process

• Understand the application domain

• Identify data sources and select target data

• Pre-process: cleaning, attribute selection

• Data mining to extract patterns or models

• Post-process: identifying interesting or useful patterns

• Incorporate patterns in real world tasks

Data mining applications

• Marketing, customer profiling and retention, identifying potential customers, market segmentation.

• Fraud detection

• identifying credit card fraud, intrusion detection

• Scientific data analysis

• Text and web mining

• Any application that involves a large amount of data …

Web data extraction

Align and extract data items (e.g., region1)

Opinion Analysis

• Word-of-mouth on the Web

• The Web has dramatically changed the way that consumers express their opinions.

• One can post reviews of products at merchant sites, Web forums, discussion groups, blogs

• Techniques are being developed to exploit these sources.

• Benefits of Review Analysis

• Potential Customer: No need to read many reviews
• Product manufacturer: market intelligence, product benchmarking

Feature Based Analysis & Summarization

• Extracting product features (called Opinion Features) that have been commented on by customers.

• Identifying opinion sentences in each review and deciding whether each opinion sentence is positive or negative.

• Summarizing and comparing results.

An example

• GREAT Camera., Jun 3, 2004

• Reviewer: jprice174 from Atlanta, Ga.

• I did a lot of research last year before I bought this camera... It kinda hurt to leave behind my beloved nikon 35mm SLR, but I was going to Italy, and I needed something smaller, and digital.

• The pictures coming out of this camera are amazing. The 'auto' feature takes great pictures most of the time. And with digital, you're not wasting film if the picture doesn't come out. …

• ….

Visual Comparison