Katharina Tschumitschew

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A new clustering algorithm that identifies clusters step by step is introduced. It is based on the principles of noise clustering dividing the data set into a good cluster and the remaining data that might contain only noise or also other clusters. The algorithm can be applied to finding just a few substructures (clusters), but also as an iterative method(More)
Visualisation is usually one of the first steps in handling any data analysis problem. Visualisations are an intuitive way to discover inconsistencies, out-liers, dependencies, interesting patterns and peculiarities in the data. However, due to modern computer technology, a vast number of visualisation techniques is available nowadays. Even if only simple(More)
A heuristic approach to possibilistic clustering is the effective tool for the data analysis. The approach is based on the concept of allotment among fuzzy clusters. To establish the number of clusters in a data set, a validity measure is proposed in this paper. An illustrative example of application of the proposed validity measure to the Anderson's Iris(More)