? to introduce the basic data mining process which includes feature selection, supervised learning, unsupervised learning, association rule mining and inference
? to discuss the operations research background of popular hard and fuzzy clustering algorithms
? to derive multiple linear regression (MLR) as a tool in supervised learning process
? to introduce the operations research background of logistic regression as a classification algorithm.
? to introduce the details of popular classifiers such as naive bayes, instance based and decision tree based classifiers.
? to discuss the details of association rule mining.
? to introduce various intelligent search algorithms such as simulated annealing, genetic algorithms, tabu search, GRASP and beam search and how they are used as part of wrappers in the context of feature selection process.
? to introduce multicriteria decision making and how some MCDM algorithms can be incorporated in the data mining process
? to experience WEKA as a software tool in data mining.
? to expose the students to real world applications through case studies.
? to develop students? team working skills, as well as self-confidence, in dealing with decision making problems.