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 ...

• Type: Conference
• Year: 2008
• Where: AUSDM
• Authors: Chowdhury Farhan Ahmed, Syed Khairuzzaman Tanbeer, Byeong-Soo Jeong, Young-Koo Lee
• Keywords: AUSDM 2008, Candidate Patterns, Data Mining, Database Scans, Share-frequent Pattern Mining