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The course will address unsupervised learning, supervised
learning, association rule mining and feature subset
selection problems, focus on the optimization formulations
of these problems, discuss various techniques proposed as
solutions and present their implementation particularly in
the context of operations management. Among others,
probabilistic and statistical methods, possibilistic methods
clustering algorithms, decision trees, metaheuristics (such
as genetic algorithms, simulated annealing, etc.) and
mathematical programming will be covered as part of the
toolbox that are widely utilized in data mining. As part of
the course multi criteria decision making and multi
objective optimization, and their usage in data mining will
also be covered. The course will include case studies from
both manufacturing and service industries.
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