Data Mining and Knowledge Discovery

The amount of information that is being created and stored in the computer is increasing very rapidly. Everyday transactions, protocols, and documents are being stored in the computer and automated monitoring systems create vast information repositories.
With the advent of the internet, these information resources have become available to individuals and companies regardless of national borders and constraints of time and space. As a consequence, information overload is rapidly becoming the new plague of the information society. It is, therefore, becoming increasingly important to provide effective tools to help users organize, manage, understand, and access large repositories of information.

• Clustering of Categorical and Continuous Data
  
  In this research project, algorithms should be developed for an effective and efficient cluster analysis on any combination of continuous and categorical attributes.
  
  
• HD-Eye: Visual Clustering of High Dimensional Data
  
  Clustering of large data bases is an important research area with a large variety of applications in the data base context. Missing in most of the research efforts are means for guiding the clustering process and understanding the results, which is especially important for high dimensional data. Visualization technology may help to solve this problem.
  
  
• High Dimensional Clustering and "Projected Clustering"
  
  Many applications require the clustering of large amounts of high-dimensional data. However, most available algorithms are appropriate only for a specific definition of clusters such as (full-dimensional) gaussian or single linkage clusters or specific notions of projected clusters. Within the scope of this project, a consistent framework for clustering is developed, which generalizes a wide variety of cluster definitions.