DAGger: clustering correlated uncertain data (to predict asset failure in energy networks)
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
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Data uncertainty is common in real-world applications. Various reasons lead to data uncertainty, including imprecise measurements, network latency, outdated sources and sampling errors. These kinds of uncertainties have to be handled cautiously, or else the data mining results could be unreliable or wrong. In this demo, we will show uRule, a new rule-based classification and prediction system for uncertain data. This system uses new measures for generating, pruning and optimizing classification rules. These new measures are computed considering uncertain data intervals and probability distribution functions. Based on the new measures, the optimal splitting attributes and splitting values can be identified and used in classification rules. uRule can process uncertainty in both numerical and categorical data. It has satisfactory classification performance even when data is highly uncertain.