ClassInfo

ECT 584 Web Data Mining

Bamshad Mobasher

Office: CDM 833
Fall 2012-2013
Class number: 15973
Section number: 701
M 5:45PM - 9:00PM
LEWIS 01111 Loop Campus

Summary

Web mining refers to the discovery of interesting and useful knowledge from the data associated with the usage, content, and the structure of Web resources. It has quickly become one of the most important areas in Computer and Information Sciences because of its direct applications in e-commerce, e-CRM, Web analytics, information retrieval/filtering, and Web information systems. 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 business intelligence, site management, personalization, and user profiling. The course will also provide a brief overview of other areas in Web mining, such as Web content mining and Web structure mining. The first half of the course will be focused on a detailed overview 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 recommender systems. This course can count as an advanced course for Computer Science students in AI concentrations.



Texts



Grading

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

  • Assignments = 65%
  • Final Project = 35%


Prerequisites

Some background in basic statistics and data structures; basic knowledge of database design and programming.



Project

For the class project, students can choose to do an implementation project, a data analysis project, or a research paper. Implementation projects may be done individually or in groups of 2 people (depending the complexity and the type of the project). Research papers and data analysis projects must be done individually. Each group or individual will submit a specific project proposal to be approved. More details about the possible project options, as well as due dates for the proposal and the final submission, will be available on the class Web site.



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.

  • Data Mining and Knowledge Discovery
    • The KDD process and methodology
    • Data preparation for knowledge discovery
    • Overview of data mining techniques
    • Market basket analysis
    • Classification and prediction
    • Clustering
    • Memory-based reasoning
    • Evaluation and interpretation

  • Web Usage Mining Process and Techniques
    • Data collection and sources of data
    • Data preparation for usage mining
    • Mining navigational patterns
    • Integrating e-commerce data
    • Leveraging site content and structure
    • User tracking and profiling
    • E-Metrics: measuring success in e-commerce
    • Privacy issues

  • Web Mining Applications and Other Topics
    • Data integration for e-commerce
    • Web personalization and recommender systems
    • Web content and structure mining
    • Web data warehousing


  • School policies:

    Changes to Syllabus

    This syllabus is subject to change as necessary during the quarter. If a change occurs, it will be thoroughly addressed during class, posted under Announcements in D2L and sent via email.

    Online Course Evaluations

    Evaluations are a way for students to provide valuable feedback regarding their instructor and the course. Detailed feedback will enable the instructor to continuously tailor teaching methods and course content to meet the learning goals of the course and the academic needs of the students. They are a requirement of the course and are key to continue to provide you with the highest quality of teaching. The evaluations are anonymous; the instructor and administration do not track who entered what responses. A program is used to check if the student completed the evaluations, but the evaluation is completely separate from the student’s identity. Since 100% participation is our goal, students are sent periodic reminders over three weeks. Students do not receive reminders once they complete the evaluation. Students complete the evaluation online in CampusConnect.

    Academic Integrity and Plagiarism

    This course will be subject to the university's academic integrity policy. More information can be found at http://academicintegrity.depaul.edu/ If you have any questions be sure to consult with your professor.

    All students are expected to abide by the University's Academic Integrity Policy which prohibits cheating and other misconduct in student coursework. Publicly sharing or posting online any prior or current materials from this course (including exam questions or answers), is considered to be providing unauthorized assistance prohibited by the policy. Both students who share/post and students who access or use such materials are considered to be cheating under the Policy and will be subject to sanctions for violations of Academic Integrity.

    Academic Policies

    All students are required to manage their class schedules each term in accordance with the deadlines for enrolling and withdrawing as indicated in the University Academic Calendar. Information on enrollment, withdrawal, grading and incompletes can be found at http://www.cdm.depaul.edu/Current%20Students/Pages/PoliciesandProcedures.aspx.

    Students with Disabilities

    Students who feel they may need an accommodation based on the impact of a disability should contact the instructor privately to discuss their specific needs. All discussions will remain confidential.
    To ensure that you receive the most appropriate accommodation based on your needs, contact the instructor as early as possible in the quarter (preferably within the first week of class), and make sure that you have contacted the Center for Students with Disabilities (CSD) at:
    Lewis Center 1420, 25 East Jackson Blvd.
    Phone number: (312)362-8002
    Fax: (312)362-6544
    TTY: (773)325.7296