Slim: Directly Mining Descriptive Patterns

Koen Smets, Jilles Vreeken

Abstract: Mining small, useful, and high-quality sets of patterns has recently become an important topic in data mining. The standard approach is to first mine many candidates, and then to select a good subset. However, the pattern explosion generates such enormous amounts of candidates that by post-processing it is virtually impossible to analyse dense or large databases in any detail. We introduce Slim, an any-time algorithm for mining high-quality sets of itemsets directly from data. We use MDL to identify the best set of itemsets as that set that describes the data best. To approximate this optimum, we iteratively use the current solution to determine what itemset would provide most gain—estimating quality using an accurate heuristic. Without requiring a pre-mined candidate collection, Slim is parameter-free in both theory and practice. Experiments show we mine high-quality pattern sets; while evaluating orders-of-magnitude fewer candidates than our closest competitor, Krimp, we obtain much better compression ratios—closely approximating the locally-optimal strategy. Classification experiments independently verify we characterise data very well.