Similarity-based learning on structures

The seminar centered around di erent aspects of similarity-based clustering with the special focus on structures. This included theoretical foundations, new algorithms, innovative applications, and future challenges for the eld. For nding the structure in the data set's smothers many tools are related like sisters and brothers. We conclude in the sequel: All methods are equal! (But some are more equal than others.) 1 Goals of the Seminar Similarity-based learning methods have a great potential as an intuitive and exible toolbox for mining, visualization, and inspection of large data sets across several disciplines. While state-of-the-art methods o er e cient solutions for a variety of problems such as the inspection of huge data sets occuring in genomic pro ling, satellite remote sensing, medical image processing, etc. a number of important questions requires further research. The detection, adequate representation, and comparison of structures turned out to be one key issue in virtually all applications. Frequently, learning data contain structural information such as spatial or temporal dependencies, higher order correlations, relational dependencies, or complex causalities. Thus, learning algorithms have to cope with these data structures. In this context, various qualitatively different aspects can be identi ed: often, data are represented in a speci c structured format such as relational databases, XML documents, symbolic sequences, and the like. Similarity based learning has to identify appropriate preprocessing or similarity measures which facilitate further processing. Several problem formulations are ill-posed in the absence of additional structural information e.g. due to a limited