Date of Original Version
Abstract or Description
Association Rule Mining algorithms operate on a data matrix (e.g., customers \Theta products) to derive association rules [2, 23]. We propose a new paradigm, namely, Ratio Rules, which are quantifiable in that we can measure the "goodness" of a set of discovered rules. We propose to use the "guessing error" as a measure of the "goodness", that is, the rootmean -square error of the reconstructed values of the cells of the given matrix, when we pretend that they are unknown. Another contribution is a novel method to guess missing /hidden values from the Ratio Rules that our method derives. For example, if somebody bought $10 of milk and $3 of bread, our rules can "guess" the amount spent on, say, butter. Thus, we can perform a variety of important tasks such as forecasting, answering "what-if" scenarios, detecting outliers, and visualizing the data.