Abstract
The applicability of potential functions in unsupervised pattern recognition is demonstrated on the basis of a new clustering technique called CLUPOT. CLUPOT is a centrotype sorting technique which means that for each detected cluster of objects a representative object can be selected. CLUPOT uses a reliability curve which permits the detection of significant clusters. Applications to four data sets (Kowalski's archeological artefact data, Ruspini's fuzzy set data. Fisher's Iris data and a part of Esbensen's meteorite data) show that CLUPOT yields significant clusterings.
Published Version
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