There has been a considerable growth in the use of Statistical Process Control (SPC) for improving the quality in business, industries, or software development since the last decade. However, the processes are growing much more complex, and there is a tremendous increase of data size owning to the use of automated record machine. The conventional SPC tools become less effective in analyzing and identifying the cause of the process failures. This paper extends the idea of the Modified Centered CUSUMS, and proposes a new data selection procedure so that the associative discovery technique can be used in retrospective SPC analysis. Through our approach, the common data mining method (i.e. associative discovery) can be used to find the hidden knowledge from the data, and identify the causes of the process failure or success for the quality improvement. Besides, the hidden information that we extracted from the data can be used as supplement for the cause and effect diagram in the on-line process control.