CIS664: Knowledge Discovery and Data Mining

Spring 2007

Course Information - Topics

Current technology has made available enormous amounts of data. Many organizations are creating huge databases of business data, such as consumer data, transaction histories, etc. Scientists and engineers in many fields are capturing increasingly complex experimental data sets. But why do people store so much data? The main objective is to extract (or mine) interesting patterns, associations, rules, changes, anomalies, and general regularities from the data to improve the process of decision making. In this course we will study the tools needed for efficient inference of these types of knowledge from massive data sets.

Topics covered include:

Special emphasis will be given to multimedia, business, scientific, and medical databases. You can find a tentative outline of the course here.






Method of evaluation

Late policy: The project parts and homework assignments are due in class on the specified due date. No late submissions will be accepted. For fairness, this policy will be strictly enforced.

Exams: All aids are allowed (open books, open notes, caclulators, etc).


Important Dates

Honor Code


I encourage students with disabilities, including "invisible" disabilities such as chronic diseases and learning disabilities, to discuss with us any appropriate accomodations that we might make on their behalf. Student must provide me with a note from the office of Disability Resources and Services at in 100 Ritter Annex, 215-204-1280, regarding their disability.

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