Abstract.
Association Rule Mining algorithms operate on a data matrix (e.g., customers \(\times\) products) to derive association rules [AIS93b, SA96]. 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 also propose the “guessing error” as a measure of the “goodness”, that is, the root-mean-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 butter. Thus, unlike association rules, Ratio Rules can perform a variety of important tasks such as forecasting, answering “what-if” scenarios, detecting outliers, and visualizing the data. Moreover, we show that we can compute Ratio Rules in a single pass over the data set with small memory requirements (a few small matrices), in contrast to association rule mining methods which require multiple passes and/or large memory. Experiments on several real data sets (e.g., basketball and baseball statistics, biological data) demonstrate that the proposed method: (a) leads to rules that make sense; (b) can find large itemsets in binary matrices, even in the presence of noise; and (c) consistently achieves a “guessing error” of up to 5 times less than using straightforward column averages.
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Received: March 15, 1999 / Accepted: November 1, 1999
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Korn, F., Labrinidis, A., Kotidis, Y. et al. Quantifiable data mining using ratio rules. The VLDB Journal 8, 254–266 (2000). https://doi.org/10.1007/s007780050007
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DOI: https://doi.org/10.1007/s007780050007