Abstract
Recent advances in rough sets theories focused much effort in the area incorporation of the innovative solutions into the rough set based frameworks. In this context, Rough Extended Framework - REF has been introduced and primarily developed in the area of the data structure exploration by the means of examining of metric dependencies between analysed rough data approximations and the reference set. The reference sets most often are composed of certain number of clusters or thresholds that are selected objects of the input data space. In case of applying the second of the above-mentioned procedures: thresholding methodology - rough thresholding measures are created that present the subject and a part of Rough Extended Thresholding Framework - T-REF.
The paper deals with the problem of extension of non-correlated Gauss Distribution based Rough Entropy Thresholding Measures into correlated measures. Additionally, in the present study the algorithm for maximizing this measure has been proposed and described. The experimental setup and experimental results are given in order to present the introduced rough entropy thresholding measures. The paper is concluded by the summary of the introduced concepts.
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Małyszko, D., Stepaniuk, J. (2011). Correlated Gauss Distribution Based Rough Entropy Thresholding Measures in Image Segmentation. In: Ryżko, D., Rybiński, H., Gawrysiak, P., Kryszkiewicz, M. (eds) Emerging Intelligent Technologies in Industry. Studies in Computational Intelligence, vol 369. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22732-5_20
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DOI: https://doi.org/10.1007/978-3-642-22732-5_20
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