Abstract:
Educational data mining is discovering
knowledge from data that come from educational
environment. This paper presents finding interesting
patterns from educational database. Learning how
student behaviors relate to academic results, could
improve the teaching system. Association rule mining
is used to find the interesting patterns, from which
student behaviors can be learned. Correlation value,
measured by lift ratio is used to measure the
interestingness. The statistical index of the degree to
which two variables are associated is the correlation
coefficient. The lift value of greater than 1 indicates
a positive correlation between antecedent and
consequent. FP Growth algorithm is used to
implement the association rule.
