We are given a large database of customer transactions. Each transaction consists of items purchased by a customer in a visit. We present an efficient algorithm that generates all significant association rules between items in the database. The algorithm incorporates buffer management and novel estimation and pruning techniques. We also present results of applying this algorithm to sales data obtained from a large retailing company, which shows the effectiveness of the algorithm.

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. Moreover, we show how to compute Ratio Rules in a single pass over the dataset with small memory requirements (a few small matrices), in co...

Association Rule Mining algorithms operate on a data matrix (e.g., customers \Theta products) to derive rules [2, 22]. We propose a single-pass algorithm for mining linear rules in such a matrix based on Principal Component Analysis. PCA detects correlated columns of the matrix, which correspond to, e.g., products that sell together. The first contribution of this work is that we propose to quantify the "goodness" of a set of discovered rules. We define the "guessing error": the root-mean-square error of the reconstructed values of the cells of the given matrix, when we pretend that they are unknown. The second contribution is a novel method to guess missing/hidden values from the linear 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, `what-if' scenarios, outlier detection, and visualization. Moreover, we show that we...

ABSTRACT: Association rules are one of the most researched areas of data mining and have recently received much attention from the database community. They have proven to be quite useful in the marketing and retail communities as well as other more diverse fields. In this paper we provide an overview of association rule research. 1