By the end of this course, you will be able to; develop a Data Mining workflow for solving a clustering problem as well as for extracting potentially interesting association rules. You will be able to use the appropriate proximity measure, and to select the "optimal clustering model" (whatever it means) to solve a clustering problem. Furthermore, you will be able to develop a Data Mining workflow to extract potentially interesting association rules. You will learn all this by using the KNIME open source platform, which integrates power and expressiveness of Weka, R and Java.
The course spans four weeks. Each week requires 8 to 10 hours of work. Each week consists of 3 to 5 lectures. Each lecture consists of a methodology video, a software usage video and a practice session.
Basic knowledge of probability and statistics. Basic knowledge of R programming.