The notion of heavy hitters-items that make up a large fraction of the population-has been successfully used in a variety of applications across sensor and RFID monitoring, network data analysis, event mining, and more. Yet this notion often fails to capture the semantics we desire when we observe data in the form of correlated pairs. Here, we are interested in items that are conditionally frequent: when a particular item is frequent within the context of its parent item. In this work, we introduce and formalize the notion of Conditional Heavy Hitters to identify such items, with applications in network monitoring, and Markov chain modeling. We introduce several streaming algorithms that allow us to find conditional heavy hitters efficiently, and provide analytical results. Different algorithms are successful for different input characteristics. We perform an experimental evaluation to demonstrate the efficacy of our methods, and to study which algorithms are most suited for different types of data.
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