Basic principles and methods for data analysis and
knowledge discovery. Emphasizes developing basic skills for modeling
and prediction, on one side, and performance evaluation, on the other.
Topics include system design; data quality, preprocessing, and
association; event classification; clustering; biometrics; business
intelligence; and mining complex types of data.
Engineering Building 4419
jessica [AT] cs [DOT] gmu [DOT] edu
jmistry2 [AT] gmu [DOT] edu
Office Hours: TBA
Art & Design Building 2026
- The ability to apply
computing principles, probability and statistics relevant to the data
mining discipline to analyze data.
- A thorough understanding
programming with data mining tools, algorithms for estimation,
prediction, and pattern discovery.
- The ability to analyze a
identifying and defining the computing requirements appropriate to its
solution: data collection and preparation, functional requirements,
selection of models and prediction algorithms, software, and
- The ability to
performance metrics used in the data mining field to interpret the
results of applying an algorithm or model, to compare methods and to
reach conclusions about data.
- The ability to
effectively to an audience the steps and results followed in solving a
data mining problem (through a term project)
Grade of C
or better in CS 310 and STAT 344
There will be two midterm exams and one final exam
covering lectures and
readings. All exams will be in class, closed book. The final exam is
must be taken at the scheduled time and place, unless prior arrangement
has been made with the instructor. Missed exams cannot be
is in effect at all times. In addition, the CS Department has further
honor code policies regarding programming projects, which are detailed here.
Any deviation from the GMU or the CS department Honor Code is
an Honor Code violation.
to Data Mining by Pang-Ning Tan, Michael
Steinbach, and Vipin Kumar
Mining and Analysis by Mohammed Zaki (Here
is the online pdf
Ch.5: Classification: Alternative Techniques
Ch.6: Association Analysis: Basic Concepts and Algorithms
Ch.7: Association Analysis: Advanced Concepts
Ch.8: Cluster Analysis: Basic Concepts and Algorithms
Ch.9: Cluster Analysis: Additional Issues and Algorithms
Ch.10: Anomaly Detection