Data Mining and Data Warehousing

At the end of the course, students should be able to: 1. describe what data mining is and how data mining can be employed and applied to solve real problems; 2. recognise wither a data mining solution is feasible alternative for a specific problem; 3. apply basic statistical to evaluate the results of data mining models; 4. develop a comprehensive understanding of how several data mining techniques can be applied to solve problems; and 5. identify the common designs and structures of warehouse systems.

Fundamentals of data mining, Data Mining Functionalities, Classification of Data Mining systems, Data Mining Task Primitives, Integration of a Data Mining System with a Database or a Data Warehouse System, Information retrieval, machine learning, optimisation, microeconomics, algorithms, mathematical formulas, computer programs, analytic tools and online portals, Data Cleaning, Data Integration and Transformation, Data Reduction, Discretisation and Concept Hierarchy Generation, Data Warehouse and OLAP Technology for Data Mining: Data Warehouse, Multidimensional Data Model, Data Warehouse Architecture, Data Warehouse Implementation, Further Development of Data Cube Technology, From Data Warehousing to Data Mining Data Cube Computation and Data Generalisation