Finding Association Rules by Direct Estimation of Likelihood Ratios

TL;DR

This paper introduces a new method for estimating likelihood ratios directly to find association rules, outperforming traditional methods like Apriori in data mining tasks.

Contribution

It presents a novel cost function for direct likelihood ratio estimation, improving association rule strength evaluation in data mining.

Findings

Outperforms Apriori in association rule strength estimation

Uses a cost function based on mean square error for likelihood ratio estimation

Provides a new approach for direct estimation of conditional probabilities

Abstract

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.