Abstract
Recent advances witnessed during widespread development of information systems that depend upon detailed data analysis, require more sophisticated data analysis procedures and algorithms. In the last decades, deeper insight into data structure has been made more precise by means of many innovative data analysis approaches. Rough Extended (Entropy) Framework presents recently devised algorithmic approach to data analysis based upon inspection of the data object assignment to clusters. Data objects belonging to clusters contribute to cluster approximations. Cluster approximations are assigned measures that directly make possible calculation of cluster roughness. In the next step, total data rough entropy measure is calculated on the base of the particular roughness. In the paper, in the Rough Extended (Entropy) Framework, a new family of the probabilistic rough (entropy) measures has been presented. The probabilistic approach has been extended into fuzzy domain by fuzzification of the probabilistic distances. The introduced solution seems to present promising area of data analysis, particulary suited in the area of image properties analysis.
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Małyszko, D., Stepaniuk, J. (2010). Fuzzified Probabilistic Rough Measures in Image Segmentation. In: Kim, Th., Pal, S.K., Grosky, W.I., Pissinou, N., Shih, T.K., Ślęzak, D. (eds) Signal Processing and Multimedia. MulGraB SIP 2010 2010. Communications in Computer and Information Science, vol 123. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17641-8_11
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DOI: https://doi.org/10.1007/978-3-642-17641-8_11
Publisher Name: Springer, Berlin, Heidelberg
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