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1 2002

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Data granulation through optimization of similarity measure

Bargiela A., Pedrycz W., Hirota K.

Archives of Control Sciences

2002

Vol. 12, no. 4

469-491

We introduce a logic-driven clustering in which prototypes are formed and evaluated in a sequential manner. The way of revealing a structure in data is realized by maximizing a certain performance index (objective function) that takes into consideration an overall level of matching and a similarity level between the prototypes. It is shown how the relevance of the prototypes translates into their granularity. The clustering method helps identify and quantify anisotropy of the feature space. We also show how each prototype is equipped with its own weight vector describing the anisotropy property and thus implying some ranking of the features in the data space.