| WARNING: Small changes to this syllabus may be made during the semester. |
COURSE DESCRIPTION:This course presents current research in Knowledge Discovery in Databases (KDD) dealing with data integration, mining, and interpretation of patterns in large collections of data. Topics include data warehousing and data preprocessing techniques; data mining techniques for classification, regression, clustering, deviation detection, and association analysis; and evaluation of patterns mined from data. Industrial and scientific applications are discussed.Students will be expected to read assigned textbook chapters and research papers, and work on implementation/research projects that cover the different stages of the KDD process. This course can be used to satisfy the graduate AI bin requirement.
CLASS MEETING:
Time: Tuesdays and Fridays 3:00-4:20 pm INSTRUCTOR:TEXTBOOK:
Several other books on the subject and related subjects are recommended below. Some research papers will be handed out during the semester. PREREQUISITE:GRADES:
Your final grade will reflect your own work and achievements during the course. Any type of cheating will be penalized and reported to the WPI Judicial Board in accordance with the Academic Honesty Policy. Note that this course follows the guidelines established by the WPI faculty in May 2010: "A student is expected to expend at least 56 hours of total effort for each graduate credit. This means that a student in a 3-graduate credit 14-week course is expected to expend at least 12 hours of total effort per week."Hence, please expect to have to spend at least 9 hours of work outside the classroom on this course each week. CLASS PARTICIPATIONAll students are expected to read the material assigned for each class in advance and to participate in class discussions. Also, students will take turns presenting papers and leading class discussions of assigned readings. Class participation will be taken into account when deciding students' final grades.PROJECTS, ASSIGNMENTS, AND SHOWCASESProjectsThis course is project-intensive. Several projects related to the data mining stages and/or techniques covered in the class will be assigned. Students will work on this projects individually, not in teams. Students will be required to provide both a written report and an oral (in-class) presentation describing their work on each of these projects. Datasets for those projects will be selected from online database repositories, or other sources.Several different data mining tools will be used in this course:
More detailed descriptions of the assignments and projects will be posted to the course webpage at the appropriate times during the semester. ShowcaseEach student should search for a real-world successful application of data mining and present it in class. This sucessful data mining story should be about using data mining to discover novel and useful patterns that made a difference in a certain industry or field. The application domain is up to the student (e.g., finance, sports, healthcare, science, ...). The chosen sucessful data mining story should be discussed with the professor in advance. The student will then give a 10 minute in-class presentation describing this application in as much detail as possible, focusing on its data mining aspects. Students will take turns presenting their showcases throughout the term, one student per class.CLASS MAILING LISTThe mailing list for this class is: This mailing list reaches the professor and all the students in the class. CLASS WEB PAGESThe webpages for this class are located at http://www.cs.wpi.edu/~cs548/s14/Announcements will be posted on the web pages and/or the class mailing list, and so you are urged to check your email and the class web pages frequently. WARNING:Small changes to this syllabus may be made during the course of the semester.ADDITIONAL SUGGESTED REFERENCESKnowledge Discovery and Data Mining
OTHER ONLINE RESOURCES:
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