COURSE UNIT TITLE

: DATA MINING AND KNOWLEDGE DISCOVERY

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
CSE 5006 DATA MINING AND KNOWLEDGE DISCOVERY ELECTIVE 3 0 0 9

Offered By

Graduate School of Natural and Applied Sciences

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

ASSOCIATE PROFESSOR DERYA BIRANT

Offered to

Computer Engineering Non-Thesis
COMPUTER ENGINEERING
Biomedical Tehnologies (English)
Industrial Ph.D. Program In Advanced Biomedical Technologies
Industrial Ph.D. Program In Advanced Biomedical Technologies
Computer Engineering
Computer Engineering
Computer Engineering (Non-Thesis-Evening)

Course Objective

The aim of this course is to give students the theoretical background of data mining algorithms and techniques and to give the student the ability to select and apply appropriate data mining techniques for different applications. This course will enable a student to learn data preprocessing, association rule mining, classification and prediction, and cluster analysis with applications.

Learning Outcomes of the Course Unit

1   Define basic data mining concepts
2   Apply preprocessing operations on data
3   Determine which data mining technique is appropriate to solve a particular problem
4   Design a data mining model
5   Implement a data mining algorithm

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Introduction to Data Mining
2 Data Mining: A Closer View
3 Data Preparation
4 Association Rule Mining
5 Sequential Pattern Mining
6 Classification and Prediction - I
7 Classification and Prediction - I
8 Clustering
9 Anomaly (Outlier) Detection
10 Main Data Mining Tools
11 Web Mining
12 Text Mining
13 Privacy Preserving Data Mining
14 Project presentations

Recomended or Required Reading

Textbook:
Han, J. & Kamber, M., Data Mining: Concepts and Techniques, Morgan Kaufmann Publishers, San Francisco, Third Edition,
2011.

Supplementary Book(s):
Ian H. Witten, Eibe Frank, Mark A. Hall, Christopher J. Pal, Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann, 4 edition, 2016, ISBN: 978-0128042915

Planned Learning Activities and Teaching Methods

Lectures,
Literature Review / Research,
Application Development,
Presentation,
Term project

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 ASG ASSIGNMENT
2 PRS PRESENTATION
3 FCG FINAL COURSE GRADE ASG * 0.50 + PRS * 0.50


*** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

None

Assessment Criteria

Course outcomes will be evaluated with the presentation of the student about a topic, literature review, and project / report prepared by the student.

Language of Instruction

English

Course Policies and Rules

Code writing knowledge and skills are required.
Participation is mandatory.

Contact Details for the Lecturer(s)

Assoc.Prof.Dr. Derya BIRANT
Dokuz Eylul University
Department of Computer Engineering
Tinaztepe Campus 35390 Buca / IZMIR
Tel: +90 (232) 301 74 01
E-mail: derya@cs.deu.edu.tr

Office Hours

Tuesday 9:30 - 10:30

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 14 3 42
Preparations before/after weekly lectures 14 2 28
Web Search and Library Research 1 5 5
Reading 10 3 30
Project Preparation 1 65 65
Preparing presentations 1 30 30
Preparing report 1 25 25
TOTAL WORKLOAD (hours) 225

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10PO.11
LO.145
LO.243
LO.355243
LO.441
LO.532114135

CONTACT INFORMATION
Dokuz Eylül Üniversitesi Cumhuriyet Bulvarı No: 144 35210 Alsancak / İZMİR
Phone: +90(232) 412 12 12 - Fax: +90 (232) 464 81 35

Copyright 2015 DEÜ. BİD

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