vorgelegt von

vAbstract Knowledge Discovery in Databases (KDD) is the non-trivial process of identi-fying valid, novel, potentially useful, and ultimately understandable patterns in data. The core step of the KDD process is the application of a Data Min-ing algorithm in order to produce a particular enumeration of patterns and relationships in large databases. Clustering is one of the major data mining techniques and aims at grouping the data objects into meaningful classes (clusters) such that the similarity of objects within clusters is maximized, and the similarity of objects from different clusters is minimized. This can serve to group customers with similar interests, or to group genes with related functionalities. Currently, a challenge for clustering-techniques are especially high dimen-sional feature-spaces. Due to modern facilities of data collection, real data sets usually contain many features. These features are often noisy or exhibit correlations among each other. However, since these effects in different part