Automated fault localization techniques collect runtime information as input data to identify suspicious statement potentially responsible for program failures. To discover the statistical coincidences between test results (i.e., failing or passing) and the executions of the different statements of a program (i.e., executed or not executed), researchers developed a suspiciousness methodology (e.g., spectrum-based formulas and deep neural network models). However, the occurrences of coincidental correctness (CC) which means the faulty statements were executed but the output of the program was right affect the effectiveness of fault localization. Many researchers seek to identify CC tests using cluster analysis. However, the high-dimensional data containing too much noise reduce the effectiveness of cluster analysis.