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. Particular emphasis will be given to multi criteria decision making and multi objective optimization, and their usage in data mining.The course will include case studies from both manufacturing and service industries.
Operations Research and Data Mining (IE 525)
Programs\Type | Required | Core Elective | Area Elective |
MA-European Studies | |||
MA-European Studies-Non Thesis | |||
MA-Political Science | |||
MA-Political Science-Non Thes | |||
MA-Visual Arts&Vis. Com Des-NT | |||
MA-Visual Arts&Visual Com Des | |||
MS-Bio. Sci. & Bioeng. LFI | |||
MS-Bio. Sci. & Bioeng. LFI-ENG | |||
MS-Biological Sci&Bioeng. | * | ||
MS-Business Analytics | |||
MS-Computer Sci.&Eng. LFI | |||
MS-Computer Sci.&Eng. LFI-ENG | |||
MS-Computer Science and Eng. | * | ||
MS-Cyber Security(with thesis) | * | ||
MS-Data Science | |||
MS-Elec. Eng&Comp Sc.LFI-ENG | |||
MS-Electronics Eng&Comp Sc.LFI | |||
MS-Electronics Eng&Computer Sc | * | ||
MS-Electronics Eng. | * | ||
MS-Electronics Eng. LFI | |||
MS-Electronics Eng. LFI-ENG | |||
MS-Energy Techno.&Man. | * | ||
MS-Industrial Eng. LFI-ENG | |||
MS-Industrial Engineering | * | ||
MS-Industrial Engineering LFI | |||
MS-Manufacturing Eng-Non Thes | * | ||
MS-Manufacturing Engineering | * | ||
MS-Materials Sci & Engineering | * | ||
MS-Materials Sci. & Eng. LFI | |||
MS-Materials Sci.&Eng. LFI-ENG | |||
MS-Mathematics | |||
MS-Mechatronics | * | ||
MS-Mechatronics LFI | |||
MS-Mechatronics LFI-ENG | |||
MS-Physics | |||
MS-Physics-Non Thesis | * | ||
MS-Psychology | |||
MS-Psychology-Non Thesis | |||
PHD-Biological Sci&Bioeng. | * | ||
PHD-Comp. Sci and Eng.after UG | * | ||
PHD-Computer Science and Eng. | * | ||
PHD-Cyber Security | * | ||
PHD-Electronics Eng&ComputerSc | * | ||
PHD-Electronics Eng. | * | ||
PHD-Electronics Eng. after UG | * | ||
PHD-Experimental Psychology | |||
PHD-Industrial Engineering | * | ||
PHD-Management | |||
PHD-Manufacturing Eng after UG | * | ||
PHD-Manufacturing Engineering | * | ||
PHD-Materials Sci.&Engineering | * | ||
PHD-Mathematics | |||
PHD-Mechatronics | * | ||
PHD-Mechatronics after UG | * | ||
PHD-Physics | |||
PHD-Physics after UG | |||
PHD-Social Psychology | |||
PHDBIO after UG | * | ||
PHDCYSEC after UG | * | ||
PHDEECS after UG | * | ||
PHDEPSY after UG | |||
PHDIE after UG | * | ||
PHDMAN after UG | |||
PHDMAN after UG-Finance | |||
PHDMAN after UG-Man. and Org. | |||
PHDMAN after UG-Op.&Sup. Cha. | |||
PHDMAN-Finance Area | |||
PHDMAN-Man. and Org. Area | |||
PHDMAN-Op. & Supp. Chain Area | |||
PHDMAT after UG | * | ||
PHDMATH after UG | |||
PHDSPSY after UG |
CONTENT
OBJECTIVE
To teach the optimization formulations of various data mining techniques developed for the unsupervised learning, supervised learning, association rule mining and feature subset selection problems.
LEARNING OUTCOME
list the basic components of a data mining process.
model a data mining problem and decide which techniques are suitable for the business objective of the user.
understand the optimization formulation of the various data mining problems and derive the techniques that can be used in those problems.
correctly apply the steps of various feature selection techniques, supervised and unsupervised learning algorithms and association rule mining algorithm.
implement feature selection, supervised and unsupervised learning algorithms, association rule mining techniques with a data mining tool (e.g., WEKA) in order to determine the relations that are hidden in the data.
Update Date:
ASSESSMENT METHODS and CRITERIA
Percentage (%) | |
Final | 25 |
Midterm | 25 |
Assignment | 15 |
Term-Paper | 35 |