Abstract
In this paper we present a novel method to mine the correlations of events in sensor networks to extract correlation patterns of sensors’ behaviors by using an unsupervised algorithm based on a hash table. The goal is to discover anomalous events in a large sensor network where its structure is unknown. Our algorithm enables users to select the correlation confidence level and only display the significant event correlations. Our experiment results show that it can discover significant event correlations in both continuous and discrete signals from heterogeneous sensor networks. The applications include smart building design and large network data mining.KeywordsCorrelationVisualizationSensor Network
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