Data Mining (CS 525)

2021 Spring
Faculty of Engineering and Natural Sciences
Computer Sci.& Eng.(CS)
Yücel Saygın,
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Doctoral, Master
Formal lecture,One-to-one tutorial
Interactive,Learner centered,Discussion based learning,Project based learning,Case Study
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Data mining can be viewed as lossy data reduction and learning techniques that are designed to handle massive data sets containing large numbers of categorical and numeric attributes. This course covers topics in data mining and knowledge discovery structured and unstructured databases such as data integration, mining, and interpretation of patterns, rule-based learning, decision trees, association rule mining, and statistical analysis for discovery of patterns, evaluation and interpretation of the mined patterns using visualization techniques.


? 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.


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.


  Percentage (%)
Final 15
Midterm 15
Assignment 20
Term-Paper 50



Jiawei Han
Micheline Kamber, Data mining concepts and techniques, Morgan Kaufmann

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