Australian Computer Society
Share-frequent pattern mining discovers more useful and realistic knowledge from database compared to the traditional frequent pattern mining by considering the non-binary frequency values of items in trans-actions. Therefore, recently share-frequent pattern mining problem becomes a very important research issue in data mining and knowledge discovery. Existing algorithms of share-frequent pattern mining are based on the level-wise candidate set generation-and-test methodology. As a result, they need several database scans and generate-and-test a huge number of candidate patterns. Moreover, their numbers of database scans are dependent on the maximum length of the candidate patterns.