Datamining II

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Eris Zeqo, PhD

Code
CMP 509
Name
Data Mining II
Semester
0
Lecture hours
3.00
Seminar hours
1.00
Laborator hours
0.00
Credits
3.50
ECTS
6.00
Description

“Data Mining II” focuses on advanced knowledge discovery techniques including big data analysis, advanced classification, clustering, anomaly detection, and mining data streams. The emphasis is on applying state-of-the-art algorithms in real-world contexts.

Objectives

To explore advanced data analysis techniques. To apply classification and clustering algorithms in complex scenarios. To analyze streaming and unstable data. To understand anomaly detection and personalization techniques.

Java
Tema
1
Introduction & Review of Data Mining I
2
Advanced Classification: Boosting and Bagging
3
Feature Selection & Dimensionality Reduction
4
Fuzzy Clustering and DBSCAN
5
Mining Data Streams
6
Anomaly and Outlier Detection
7
Personalization and Recommendation Systems
8
Midterm Exam
9
Text and Unstructured Data Mining
10
Ensemble Methods: Random Forests, Stacking
11
Performance Evaluation & Cross-Validation
12
Data Mining in Big Data Contexts
13
Practical Tools (Weka, Scikit-learn, RapidMiner)
14
Ethics and Privacy in Data Analysis
15
Final Project Presentations / Review
16
Final Exam
1
Students will be able to apply advanced data mining techniques on real-world and large-scale datasets.
2
They will be able to choose appropriate algorithms for classification, clustering, or anomaly detection.
3
They will use practical tools for both structured and unstructured data analysis.
4
They will understand the importance of ethics and privacy protection in data analysis.
Quantity Percentage Total percent
Midterms
0 0% 0%
Quizzes
0 0% 0%
Projects
1 20% 20%
Term projects
1 20% 20%
Laboratories
0 0% 0%
Class participation
0 0% 0%
Total term evaluation percent
40%
Final exam percent
60%
Total percent
100%
Quantity Duration (hours) Total (hours)
Course duration (including exam weeks)
16 4 64
Off class study hours
14 2 28
Duties
2 24 48
Midterms
0 0 0
Final exam
1 10 10
Other
0 0 0
Total workLoad
150
Total workload / 25 (hours)
6.00
ECTS
6.00