The association rules search of Indonesian university graduate’s data using FP-growth algorithm
Sharfina Faza, Romi Fadillah Rahmat, Erna Budhiarti Nababan, Dedy Arisandi, S Effendi · IOP Conference Series Materials Science and Engineering · 2018
The attribute varieties in university graduates data have caused frustrations to the institution in finding the combinations of attributes that often emerge and have high integration between attributes.Association rules mining is a data mining technique to determine the integration of the data or the way of a data set affects another set of data.By way of explanation, there are possibilities in finding the integration of data on a large scale.Frequent Pattern-Growth (FP-Growth) algorithm is one of the association rules mining technique to determine a frequent itemset in an FP-Tree data set.From the research on the search of university graduate's association rules, it can be concluded that the most common attributes that have high integration between them are in the combination of State-owned High School outside Medan, regular university entrance exam, GPA of 3.00 to 3.49 and over 4-year-long study duration.