Extended Association Rule Mining with Correlation Functions
Hidekazu Saito, Akito Monden, Zeynep Yücel · 2018
This paper proposes extended association rule mining that can deal with correlation functions. The extended association rule is expressed in the form of: A ⇒ Correl(X, Y ) where Correl(X, Y ) is a correlation function with two variables X and Y . By this extension, data analysts can discover the condition A that lead to low (or high) correlation between two given variables from a large dataset. In order to show the efficacy of the proposed method, a case study is performed on an industry dataset of software developments, assuming the scenario of discovering a condition, where software development effort is predictable (or unpredictable) from the size of the project, i.e. there exists a significantly high (or low) correlation between size and effort. Since such a condition cannot be obtained by conventional association rule mining, we confirm the efficiency of the proposed extended association rule mining.