| Course Description : | This course provides the students with an introduction to data mining and knowledge discovery (KDD). The course will focus on issues relating to the feasibility, usefulness, effectiveness, and scalability of techniques for the discovery of patterns hidden in large data sets. The students will learn the basic concepts of data pre-processing, frequent pattern mining and association rules, sequential patterns, and sub-graph patterns; and explore their applications, Classification methods, such as decision trees, k-nearest neighbor, and Naïve Bayes, ensemble learning methods such as random forest …etc., outlier detection methods, such as Simple Statistical Methods and local outlier factor (LOF), cluster analysis techniques, such as k-means, hierarchical methods, and density-based methods. This course is a lab-based course, Active learning methodologies will be applied through role playing, presentations and problem-solving exercises. The course contains a practical application on data mining through small practical project to cover the all previously taught techniques. |