ShrFP-Tree: An Efficient Tree Structure for Mining Share-Frequent Patterns

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 transactions. 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-andtest 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. In this paper, we propose a novel tree structure ShrFP-Tree (Share-frequent pattern tree) for share-frequent pattern mining. It exploits a pattern growth mining approach to avoid the level-wise candidate set generation-and-test problem and huge number of candidate generation. Its number of database scans is totally independent of the maximum length of the candidate patterns. It needs maximum three database scans to calculate the complete set of share-frequent patterns. Extensive performance analyses show that our approach is very efficient for share-frequent pattern mining and it outperforms the existing most efficient algorithms.