Seno, MasakazuKarypis, George2020-09-022020-09-022003-01-27https://hdl.handle.net/11299/215547Finding prevalent patterns in large amount of data has been one of the major problems in the area of data mining. Particularly, the problem of finding frequent itemset or sequential patterns in very large databases has been studied extensively over the years, and a variety of algorithms have been developed for each problem. The key feature in most of these algorithms is that they use a constant support constraint to control the inherently exponential complexity of these two problems. In general,patterns that contain only a few items will tend to be interesting if they have a high support, whereas long patterns can still be interesting even if their support is relatively small. Ideally, we want to find all the frequent patterns whose support decreases as a function of theirlength without having to find many uninteresting infrequent short patterns. Developing such algorithms is particularly challenging because the downward closure property of the constant support constraint cannot be used to prune short infrequent patterns. In this paper we present two algorithms, LPMiner and SLPMiner. Given alength-decreasing support constraint, LPMiner finds all the frequentitemset patterns from an itemset database, and SLPMiner finds all thefrequent sequential patterns from a sequential database. Each of thesetwo algorithms combines a well-studied efficient algorithm forconstant-support-based pattern discovery with three effective databasepruning methods that dramatically reduce the runtime. Our experimentalevaluations show that both LPMiner and SLPMiner, by effectively exploitingthe length-decreasing support constraint, are up to two orders of magnitudefaster, and their runtime increases gradually as the average length ofthe input patterns increases.en-USReport