CSE622

Course	CSE622
Title	Advanced Database Systems
Credits	3 - credits
Course Coordinator	Dr. Anita Wasilewska
Description	The course will cover selected topics on the cutting edge of database technology, such as deductive database query languages and systems, object-oriented data models, persistent programming languages, heterogeneous databases, and advanced transaction models.
Course Outcomes	Data Mining, called also Knowledge Discovery in Databases (KDD) is a new multidisciplinary field. It brings together research and ideas from database technology, machine learning, neural networks, statistics, pattern recognition, knowledge based systems, information retrieval, high-performance computing, and data visualization. Its main focus is the automated extraction of patterns representing knowledge implicitly stored in large databases, data warehouses, and other massive information repositories. The course will closely follow the book and is designed to give a broad, yet in-depth overview of the Data Mining field and examine the most recognized techniques in a more rigorous detail.
Textbook	Jiawei Han and Micheline Kamber, DATA MINING Concepts and Techniques, Morgan Kaufman Publishers, 2001.
Major Topics Covered in Course	General overview: What is Data Mining; which data, what kinds of patterns can be mined. Data Warehouse and OLAP technology for Data Mining. Data preprocessing: Data cleaning; data integration and transformation; data reduction; discretization and concept hierarchy generation. Data Mining primitives, languages and system architectures. Concept descriptions: Characteristic and discriminant rules; data generalization Mining association rules in large databases; transactional databases and apriori algorithm. Classification and prediction: Decision tree induction; rough sets; Bayesian classification; classification based on concepts from association rule mining; classifiers; genetic algorithms. Cluster analysis; a categorization of major clustering methods.
Laboratory
Course Webpage	CSE622