Existing data analytic techniques are mostly based on building the same one model of variable relations over the full ranges of all variable values, although relations of variables may exist only for certain values of variables or different relations exist for different values of variables. This paper presents the Partial-Value Association Discovery (PVAD) algorithm which discovers variable relations/associations that exist in partial ranges of variable values from large amounts of data in a computationally efficient way. The PVAD algorithm allows building a structural model of partial- and full-value variable associations in multiple layers that captures individual and interactive effects of multiple variables by learning from data. The application of the PVAD algorithm to the analysis of engineering student data for engineering retention is also presented.
- engineering education
- interactive effects of variables
- multi-layer model
- variable association
ASJC Scopus subject areas
- Control and Systems Engineering