The objective of this course is to give a comprehensive and systematic description of the core methods for data clustering with an emphasis on the new advances and open challenges. During the course various problems and scenarios will be explored pertaining to text, multimedia, biological, categorical, network, streams, and uncertain data.
The course is intended to be particularly interesting to computer scientists and applied mathematicians working on data-intensive applications.
We will start with an introduction to cluster analysis and goes on to explore proximity measures, hierarchical clustering, partition clustering, kernel-based clustering, sequential data clustering, large-scale data clustering, data visualization, high-dimensional data clustering and cluster validation. A set of application will be selected from different domains.