In this paper, we propose an association rule A ) Correl(X, Y ) that handles a correlation function, where A is the prerequisite and Correl(X, Y ) is the correlation between variables X and Y . With this extension, we can find conditions whose correlation of arbitrary two variables is high (or low) from a given data set. Furthermore, in order to distinguish statistically significant correlation, we define the rule A ) TestCorrel(X, Y ) which holds the result of the correlation significance test in the conclusion section, where TestCorrel(X, Y ) is a p-value of no-correlation test between X and Y . In order to confirm the feasibility of the proposed method, a case study using software development data was conducted. We found that it is possible to distinguish projects that are suitable for predicting development effort and those that are not.
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