This paper proposes extended association rule mining that can deal with correlation functions. The extended association rule is expressed in the form of: A double right arrow 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.
Extended Association Rule Mining with Correlation Functions
Yucel, Zeynep
2018-01-01
Abstract
This paper proposes extended association rule mining that can deal with correlation functions. The extended association rule is expressed in the form of: A double right arrow 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.File | Dimensione | Formato | |
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