636-0019-00L Data Mining II
|Spring Semester 2018
|K. M. Borgwardt
|yearly recurring course
|Language of instruction
|Prerequisites: Basic understanding of mathematics, as taught in basic mathematics courses at the Bachelor`s level. Ideally, students will have attended Data Mining I before taking this class.
|Data Mining, the search for statistical dependencies in large databases, is of utmost important in modern society, in particular in biological and medical research. Building on the basic algorithms and concepts of data mining presented in the course "Data Mining I", this course presents advanced algorithms and concepts from data mining and the state-of-the-art in applications of data mining.
|The goal of this course is that the participants gain an advanced understanding of data mining problems and algorithms to solve these problems, in particular in biological and medical applications, and to enable them to conduct their own research projects in the domain of data mining.
|The goal of the field of data mining is to find patterns and statistical dependencies in large databases, to gain an understanding of the underlying system from which the data were obtained. In computational biology, data mining contributes to the analysis of vast experimental data generated by high-throughput technologies, and thereby enables the generation of new hypotheses.
In this course, we will present advanced topics in data mining and its applications in computational biology.
Tentative list of topics:
1. Dimensionality Reduction
2. Association Rule Mining
3. Text Mining
4. Graph Mining
|Course material will be provided in form of slides.
|Will be provided during the course.