Course - Knowledge Discovery (KD) and Data Mining (DM) - PK8106
Knowledge Discovery (KD) and Data Mining (DM)
About
About the course
Course content
Introduction to data mining, concept and overview of steps; Preprocessing, data clearning and transformation; Clustering; Association rules; Decision trees and rule induction; Predication techniques; Visualization; Data mining strategies in Engineering and Business; Systems for data mining (IBM intelligent Miner and SPSS Clementine).
Learning outcome
Knowledge, the candidate will have knowledge related to: - Providing an introduction and basic concepts to knowledge discovery and data mining in databases, and to present real data mining applications, as well as reveal important research issues related to knowledge discovery and data mining. Skills, the candidate will have skills related to: - Understanding the fundamental concepts underlying knowledge discovery and data mining, and gain hands-on experience with implementation of some data mining algorithms applied to real world cases, such as customer relationship management, design and manufacturing, medical diagnosis and offshore business. General competence, the candidate will have general competence related to: - Understanding data mining and knowledge discovery as important theories and methods to make better decision in industry and enterprises.
Learning methods and activities
Lecturing and seminars.
Course materials
Jiawei Han and Micheline Kamber, (2001) Data Mining Concepts and Techniques, Morgen Kaufmann Publishers.
Subject areas
- Technological subjects