Optional Courses

Data Warehousing and Data Mining
Author: Date:28-02-2011 Hits:
Data Warehousing and Data Mining
Objectives and Requirements
This course aims to explore a new field and frontier in database systems, "data warehousing and data mining".  The principles, algorithms, methodology, and applications are introduced.  The components of data warehouse, including data source and transformation tools, metadata management, query reporting and OLAP are discussed.  The course also covers techniques and algorithms in data mining, including association rule mining, cluster analysis and data classification.
Upon successful completion of this course, students should be able to:
1.          Describe and discuss critical concepts and algorithms in data warehousing and data mining;
2.          Perform independent study and critical analysis and evaluation of current topics in data warehousing and data mining;  deliver and communicate their findings in a presentable format (e.g., technical report, class presentation);
3.          Design and construct standard data warehouse system;
4.          Implement existing algorithms in data mining and invent new algorithm for specific problem.
Data integration and transformation, Multi-dimensional data cube, Full cube and iceberg cube, On-line analytical processing (OLAP), Apriori based algorithms, FP-tree, k-means, k-mediods, BIRCH, DBSCAN,  Decision trees, Bayesian classifier,  Neutral network and support vector machine.

Credits: 3
Prerequisite Course(s): Database Systems.

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