  In this paper, we propose a cost function that corresponds to the mean square
errors between estimated values and true values of conditional probability in a
discrete distribution. We then obtain the values that minimize the cost
function. This minimization approach can be regarded as the direct estimation
of likelihood ratios because the estimation of conditional probability can be
regarded as the estimation of likelihood ratio by the definition of conditional
probability. When we use the estimated value as the strength of association
rules for data mining, we find that it outperforms a well-used method called
Apriori.
