Dr. Anita Wasilewska

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.

• 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.

Jiawei Han and Micheline Kamber, DATA MINING Concepts and Techniques, Morgan Kaufman Publishers, 2001.

• 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.

CSE622