Download IT433 Data Warehousing and Data Mining – Spring 2016

IŞIK UNIVERSITY
FACULTY OF ARTS and SCIENCE
IT433 Data Warehousing and Data Mining – Spring 2016
Level of Course: Undergraduate
Language of
English
Instruction:
Instructor:
Gülay Ünel, [email protected],
Office: AMF-220, Extension: 7188
Lectures:
Tuesday 9AM – 12PM
Grading Policy (Tentative)
Quizzes and Participation
Project
Midterm Exam
Final Exam
%10
%30
%30
%30
A student must get at least 50 out of 100 from Participation to pass the course.
Description
Basic methods and techniques of data mining. Relationship between databases, data
warehouses, and data mining. Data mining functionalities: association, concept
description, classification, prediction and clustering. Various algorithms for each type of
functionality such as decision tree classification, artificial neural networks, Bayesian
classification, logistic regression, K-means clustering. Applications and trends in data
mining.
Project
Each student will work on a project. The project will consist of 3 phases:
I. proposal
II. design, implementation & report
III. presentation
A student must attend the presentation to get a mark from the project report.
Cheating Policy
In case of any form of copying and cheating on assignments or exams, all involved
parties will get 0 from the assignment/exam. Cheating has serious consequences such
as suspension.
Textbook
You are responsible of the material that will be presented in the classroom. Textbook
and the schedule references are for guidance only.
Course Schedule (Tentative)
Week
1
2
3
Date
Feb. 14
Feb. 21
Feb. 28
Topics
Course overview, Introduction
Data Preprocessing
Data Preprocessing (cont.)
4
March 7
5
March 14
6
March 21
7
March 28
8
9
10
Apr. 4
Apr. 11
Apr. 18
11
12
Apr. 25
May 2
13
May 9
14
May 16
Data Warehouse and OLAP
Technology, Data Mining Tools
Tutorial I
Frequent Itemset Mining Methods
Association Mining and Correlation
Analysis
Association Mining and Correlation
Analysis (cont.)
Association Mining and Correlation
Analysis (cont.), Data Mining Tools
Tutorial II
Classification
Classification (cont.)
Prediction,
Data Mining Tools Tutorial III
Cluster Analysis
Cluster Analysis (cont.),
Data Mining Tools Tutorial IV
Applications and Trends in Data
Mining
Project Presentation
Notes
Project phase I due
Feb 28, Tuesday, 9AM
Midterm on March 21
Tuesday 9AM – 11AM
Project phase II due
May 9, Tuesday, 9AM
Project phase III