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INSTRUCTOR:

Bamshad Mobasher
Email: mobasher@cs.depaul.edu
Office: Loop Campus, CDM Building, Room 833
Phone: (312) 362-5174
Office Hours: Via email, phone, Skype, or WebEx (By Appointment)

COURSE DESCRIPTION

Web mining refers to the automatic discovery of interesting and useful patterns from the data associated with the usage, content, and the linkage structure of Web resources. It has quickly become one of the most popular areas in computing and information systems because of its direct applications in e-commerce, e-CRM, Web analytics, information retrieval/filtering, Web personalization, and recommender systems. Employees knowledgeable about Web mining techniques and their applications are highly sought by major Web companies such as Google, Amazon, Yahoo, MSN and others who need to understand user behavior and utilize discovered patterns from terabytes of user profile data to design more intelligent applications. The primary focus of this course is on Web usage mining and its applications to e-commerce and business intelligence. Specifically, we will consider techniques from machine learning, data mining, text mining, and databases to extract useful knowledge from Web data which could be used for site management, automatic personalization, recommendation, and user profiling. The first half of the course will be focused on a detailed overview of the data mining process and techniques, specifically those that are most relevant to Web mining. The second half will concentrate on the applications of these techniques to Web and e-commerce data, and their use in Web analytics, user profiling and personalization. This course also counts as an advanced course for Computer Science students concentrating in AI or Data Analysis.

PREREQUISITES

• Knowledge of basic statistics and data structures;
• Basic knowledge of database design and database programming.

TEXTBOOKS & COURSE MATERIAL

• Data Mining Techniques for Marketing, Sales, and Customer Relationship Management, Third Edition, by Michael Berry and Gordon Linoff, John Wiley, 2011.
• Recommended Books:
  • Data Mining: Practical Machine Learning Tools and Techniques, by Ian Witten and Eibe Frank, 3rd Ed., Morgan Kaufmann, 2011
  • Web Data Mining: Exploring Hyperlinks, Content, and Usage Data, by Bing Liu, 2nd Edition, , Springer, 2011 (Note: parts of the 1st edition will be available electronically for the reading assignments).

GRADING & COURSE REQUIREMENTS

The structure and grading in the class will be centered around 4-5 assignments and a final project.

The assignments focus on the concepts and techniques discussed in class. The assignments involve written or problem-oriented exercises, as well as hands on experiments with open source data mining tools, such as WEKA. These assignments must be done individually. Late assignments will be penalized 10% per day (with weekends counting as one day).

For the class project, students can choose to do one of three types of projects: an implementation project, a data analysis project, or a research paper. Implementation and data analysis projects may be done individually or in groups of up to 3 people (depending the complexity and the type of the project). Research papers must be done individually. Each group or individual will submit a specific project proposal to be approved. Student will be able to choose from among several possible projects ideas or propose their own. More details on the final project are available in the Project section.

The final grade will be determined (tentatively) based on the following components:

Assignments = 65%
Final Project = 35%

TENTATIVE LIST OF TOPICS

The following issues and topics will be covered throughout the course. Many of these topics will be revisited several times during the course in a variety of contexts.