  For Time-Domain Global Similarity (TDGS) method, which transforms the data
cleaning problem into a binary classification problem about the physical
similarity between channels, directly adopting common performance measures
could only guarantee the performance for physical similarity. Nevertheless,
practical data cleaning tasks have preferences for the correctness of original
data sequences. To obtain the general expressions of performance measures based
on the preferences of tasks, the mapping relations between performance of TDGS
method about physical similarity and correctness of data sequences are
investigated by probability theory in this paper. Performance measures for TDGS
method in several common data cleaning tasks are set. Cases when these
preference-based performance measures could be simplified are introduced.
