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In this paper, the ranking of joint roughness coefficients (JRC) profiles as well-known acceptable pattern for studying rough surfaces are investigated. For this purpose, dimension of digitized profiles was measured using fractal-wavelet based methods. Digitization of these profiles and detection of asperities has been done at a distance of 0.02 mm. The fusion of obtained results from various data fusion methods including Clone-proof Schwartz Sequential Dropping (CSSD) and graph theory with approach of scientific phenomenology showing that current trend of roughness profiles needs to be corrected. In fact, some of the exemplar profiles unlike the appearance, have a different roughness than others. This approach changes awareness about roughness as a challenging parameter. Therefore, robust answer was obtained with logical look of data fusion and presenting a new ranking for JRC profiles (JRCN).