An Efficient Algorithm for Proportionally Fault-Tolerant Data Mining

The mining of frequent patterns in databases has been studied for several years, but few reports have discussed fault-tolerant (FT) pattern mining. FT data mining is more suitable for extracting interesting information from real-world data that may be polluted by noise. This paper considers proportional FT mining of frequent patterns. The number of tolerable faults in a proportional FT pattern is proportional to the length of the pattern. Two algorithms are proposed to solve this problem. The first algorithm, named FT-BottomUp, applies an FT-Apriori heuristic and finds all FT patterns with any number of faults. The second algorithm, FT-LevelWise, divides all FT patterns into several groups according to the number of tolerable faults, and mines the content patterns of each group in turn. The experimental results show that more potential FT patterns are extracted by our approach.