Bounding and Estimating Association Rule Support From Clusters on Binary Data
The theoretical relationship between association rules and machine learning techniques needs to be studied in more depth. This explains the use of clustering as a model for association rule mining. The clustering model is exploited to bound and estimate association rule support and confidence. The authors first study the efficient computation of the clustering model with K-means; they show the sufficient statistics for clustering on binary data sets is the linear sum of points. They then prove item-set support can be bounded and estimated from the model. Finally, they show support bounds fulfill the set downward closure property. Experiments study model accuracy and algorithm speed, paying particular attention to error behavior in support estimation.