Mining fault-tolerant frequent patterns efficiently with powerful pruning

The mining of frequent patterns in databases has been studied for several years. However, the real-world data tends to be dirty and frequent pattern mining which extracts patterns that are absolutely matched is not enough. An approach, called fault-tolerant frequent pattern (FT-pattern) mining, is more suitable for extracting interesting information from real-world data that may be polluted by noise. In our approach, the problems of mining proportional and fixed FT-patterns are considered. In proportional FT-pattern mining, the number of faults tolerable in a pattern is proportional to the length of the pattern. And the number of faults tolerable in different length of patterns is fixed in fixed FT-pattern mining. A new graph structure, FT-association graph, is proposed to help us filtering out impossible candidates with high efficiency. The experimental results show that the proposed algorithms of our approach are highly efficient for mining both proportional and fixed FT-patterns.