Fault-Tolerant Frequent Pattern Mining
Fault-tolerant frequent pattern mining is a data mining approach aimed at discovering frequent patterns in large datasets containing both certain and uncertain records. Unlike traditional frequent pattern mining, which relies on exact matching based on support and confidence values, fault-tolerant mining employs approximate matching techniques to find patterns, thereby accommodating errors, missing information, or changes in the data. This approach allows for the discovery of frequent patterns even in the presence of uncertainties or faults in the dataset.
Applications: Geo-spatial Data Analysis, Remote Sensing Image Analysis, Weather Forecasting.