||Advanced Database Systems
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.
|| 3 - credits
- 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.
Jiawei Han and Micheline Kamber, DATA MINING Concepts and Techniques, Morgan Kaufman Publishers, 2001.
- 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.
|| Dr. Anita Wasilewska