The phrase "rough data" is correct and usable in written English.
This phrase is used to describe information that has not been carefully analyzed or organized. For example, "The research team gathered a lot of rough data, but still had to crunch the numbers to come up with an accurate conclusion."
Gaussian kernels are introduced to acquire fuzzy relations between samples described by fuzzy or numeric attributes in order to carry out fuzzy rough data analysis.
They have recently been shown to be an efficient tool to solve correlation equations with rough data correlations, that is, with low Sobolev smoothness or small correlation length, in almost linear time.
Rough data, derived from real scale measurements on a three-bladed Troposkien vertical-axis wind turbine, are manipulated in a convenient form to be easily compared with the typical outputs provided by simulation codes.
Therefore the rough data need to be further corrected and re-scaled to a common reference condition, according to a criterion of equal failure probability, by determining the effective area of the loaded specimen.
Codes were generated from the rough data and overarching themes or labels emerged.
Rough data were first processed using line adjust, plain adjust and average filters of the NAMA-STM Nanoanalyzer software.
Yet however rough, the data was more precise than earlier measurements taken through inventories of ground-based emissions.
The rough draft data consists of fairly short pieces of DNA, most of them more than 10,000 units in length, whose approximate position on the chromosomes is known.
In this paper we study the local and global regularity properties of the cubic nonlinear Schrödinger equation (NLS) on the half line with rough initial data.
The contributions in both parts lie in that one can allow both singular diffusion coefficient, such as the Parabolic Anderson Model, and the rough initial data, such as the Dirac delta measure.
The main statistics used in rough set data analysis, the approximation quality, is of limited value when there is a choice of competing models for predicting a decision variable.
