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Among the 26 roughness parameters described in ISO 25178 standard, the parameters used to characterize surface performance in characterization parameter set (<b>CPS</b>) lack scientificity and unity, resulting in application confusion. The current <b>CPS</b> comes from empirical selection or small sample experiments, thus featuring low generality. A new method for constructing <b>CPS</b> in rough surfaces is proposed to solve the above issues. Based on a data mining method, statistical theory, and roughness parameters definitions, the 26 roughness parameters are divided into <b>CPS</b> and redundant parameter sets (<b>RPS</b>) with the help of reconstructed surfaces and machining experiments, and the mapping relationships between <b>CPS</b> and <b>RPS</b> are established. The research shows that <b>RPS</b> accounts for 50%, and <b>CPS</b>, of great significance for surface performance, and has the ability to fully cover surface topography information. The birth of <b>CPS</b> provides an accurate parameter set for the subsequent study of different surface performance, and it provides more effective parameters for evaluating the workpiece surface performance from the same batch.