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CPE
420
Data Mining: Techniques and Applications
The course will begin with an introduction of data mining field, including why data mining, what is data mining, what kinds of data can be mined, what kinds of patterns can be mined, an overview of technologies, the major issues in data mining, and a brief history of data mining community.
Prerequisites:
0600304,0612207
0612420
(3-0-3)

Credits and Contact Hours

3 credits, 43 hours

Course Instructor Name

Dr. Ahmed Nasrallah

Textbook

Introduction to Data Mining 2nd Edition, Pang-Ning Tan, Michael Steinbach, and Vipin Kumar; Pearson, 2018. ISBN-13: 978-0133128901

Supplemental Textbook

Data Mining: Concepts and Techniques 4th Edition, Han, Jiawei, Micheline Kamber, and Jian Pei; Morgan Kaufmann, 2022. ISBN-13: 978-0128117606

Mining of Massive Datasets 2nd Edition, Jure Leskovec, Anand Rajaraman, Jeffrey David Ullman; Cambridge University Press, 2014, ISBN-13 978-1107077232

Catalog Description

This course is concerned with data mining techniques: classification (including decision tree induction, Bayes classification, k-nearest neighbor classifiers, linear classifiers, feature selection, Bayesian belief networks, support vector machines, rule-based and pattern-based classification), clustering (K-Means, Hierarchical, DBSCAN, Graph/Density-Based clustering), association (mining frequent patterns, correlation), preprocessing; performance evaluation; information assurance, deep learning, security and privacy issues, and other applications.

Prerequisite

CpE-207 and ENGR-304

Specific Goals for the Course

Upon successful completion of this course, students will be able to:

Demonstrate proficiency in various data mining techniques, including classification, clustering, and association. (Student outcomes: 1, 6)

Apply preprocessing methods to clean and prepare data for effective mining. (Student outcomes: 6)

Evaluate the performance of data mining models using relevant metrics and techniques. (Student outcomes: 1, 6)

Understand the importance of information assurance in the context of data mining. (Student outcomes: 6)

Explore web mining and its applications. (Student outcomes: 1, 6)

Analyze security and privacy issues related to data mining and propose strategies for addressing them. (Student outcomes: 1, 6)

Apply data mining techniques to real-world applications and scenarios. (Student outcomes: 1, 6)

Topics to Be Covered

Introduction to Data Mining

Data, Measurements, and Preprocessing

Data Mining Techniques: Classification

Data Mining Techniques: Association

Data Mining Techniques: Clustering

Anomaly Detection

Deep Learning

Security and Privacy Issues

Applications of Data Mining