COURSE INFORMATION Course Title: DATA MINING

Code	Course Type	Regular Semester	Theory	Practice	Lab	Credits	ECTS
CEN 571	B	1	3	2	0	4	7.5

Academic staff member responsible for the design of the course syllabus (name, surname, academic title/scientific degree, email address and signature)	NA
Main Course Lecturer (name, surname, academic title/scientific degree, email address and signature) and Office Hours:	Dr. Erind Bedalli ebedalli@epoka.edu.al , Wednesday, 17:00 - 18:00
Second Course Lecturer(s) (name, surname, academic title/scientific degree, email address and signature) and Office Hours:	NA
Teaching Assistant(s) and Office Hours:	NA
Language:	English
Compulsory/Elective:	Elective
Study program: (the study for which this course is offered)	Professional Master in Computer Engineering
Classroom and Meeting Time:	A126, Wedneday 18:00 - 20:45
Code of Ethics:	Code of Ethics of EPOKA University Regulation of EPOKA University "On Student Discipline"
Attendance Requirement:
Course Description:	Supervised learning and unsupervised clustering strategies in data mining: Data preprocessing techniques, decision trees, k-nearest neighbor, rough sets, genetic algorithms, fuzzy sets, k-means, neural-networks, Bayesian classifier, statistical techniques and association rules. Data mining in time series, text and web mining.
Course Objectives:	This course intends to provide an solid understanding and the ability to programmatically apply the fundamental concepts of data mining, including clustering, classification, frequent itemset mining, link analysis etc. Special attention is paid towards the preprocessing and postprocessing stages, in order to provide a complete journey on the data mining pipeline.

BASIC CONCEPTS OF THE COURSE

1	Data mining pipeline
2	Unsupervised learning
3	Supervised learning
4	Frequent itemset mining
5	Link analysis
6	Data postprocessing

COURSE OUTLINE

Week	Topics
1	Introduction and motivation for Data Mining
2	Data preprocessing, exploratory analysis, post-processing.
3	Distance and similarity metrics. Introduction to recommendation systems
4	Clustering. Partitional vs hierarchical clustering. K-means, agglomerative hierarchical clustering.
5	Clustering: DBSCAN algorithm. Cluster validation.
6	Basics of classification: decision trees, rule-based classification, nearest neighbor classification
7	Other classification techniques: support vector machines, naive bayes classifiers etc.
8	Midterm exam
9	Frequent itemset and association rule mining.
10	Evaluation and alternative algorithms for frequent itemset and association rule mining.
11	Link Analysis techniques for web mining: PageRank, Random Walks, HITS.
12	Data warehousing and OLAP technology.
13	Visualization Methods
14	Students' presentations

Prerequisite(s):
Textbook(s):	"Introduction to Data Mining", by Tan, Steinbach, Kumar (Pearson 2018) "Data Mining. Concepts and Techniques" by Han, Kamber, Pei
Additional Literature:
Laboratory Work:	Yes (not very frequently)
Computer Usage:	Yes (not very frequently)
Others:	No

COURSE LEARNING OUTCOMES

1	An understanding of the data mining pipeline: data collection, preprocessing, analysis and postprocessing
2	To understand, apply and interpret the results of clustering algorithms
3	To understand, apply and interpret the results of classification algorithms
4	To understand, apply and interpret the results of frequent itemset mining algorithms
5	To understand, apply and interpret the results of link analysis algorithms
6	To implement various data post-processing techniques

COURSE CONTRIBUTION TO... PROGRAM COMPETENCIES (Blank : no contribution, 1: least contribution ... 5: highest contribution)

No	Program Competencies	Cont.
Professional Master in Computer Engineering Program
1	Engineering graduates with sufficient theoretical and practical background for a successful profession and with application skills of fundamental scientific knowledge in the engineering practice.	5
2	Engineering graduates with skills and professional background in describing, formulating, modeling and analyzing the engineering problem, with a consideration for appropriate analytical solutions in all necessary situations	5
3	Engineering graduates with the necessary technical, academic and practical knowledge and application confidence in the design and assessment of machines or mechanical systems or industrial processes with considerations of productivity, feasibility and environmental and social aspects.	4
4	Engineering graduates with the practice of selecting and using appropriate technical and engineering tools in engineering problems, and ability of effective usage of information science technologies.	5
5	Ability of designing and conducting experiments, conduction data acquisition and analysis and making conclusions.	5
6	Ability of identifying the potential resources for information or knowledge regarding a given engineering issue.	4
7	The abilities and performance to participate multi-disciplinary groups together with the effective oral and official communication skills and personal confidence.	5
8	Ability for effective oral and official communication skills in foreign language.	4
9	Engineering graduates with motivation to life-long learning and having known significance of continuous education beyond undergraduate studies for science and technology.	5
10	Engineering graduates with well-structured responsibilities in profession and ethics.	4
11	Engineering graduates who are aware of the importance of safety and healthiness in the project management, workshop environment as well as related legal issues.	4
12	Consciousness for the results and effects of engineering solutions on the society and universe, awareness for the developmental considerations with contemporary problems of humanity.	4

COURSE EVALUATION METHOD

Method	Quantity	Percentage
Homework	2	7.5
Midterm Exam(s)	1	25
Presentation	1	15
Final Exam	1	40
Attendance		5
	Total Percent:	100%

ECTS (ALLOCATED BASED ON STUDENT WORKLOAD)

Activities	Quantity	Duration(Hours)	Total Workload(Hours)
Course Duration (Including the exam week: 16x Total course hours)	16	3	48
Hours for off-the-classroom study (Pre-study, practice)	16	4.5	72
Mid-terms	1	10	10
Assignments	2	10	20
Final examination	1	30	30
Other	1	7.5	7.5
Total Work Load:			187.5
Total Work Load/25(h):			7.5
ECTS Credit of the Course:			7.5

CONCLUDING REMARKS BY THE COURSE LECTURER

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