document: Mining top-k frequent items in a data stream with flexible sliding windows We study the problem of finding the k most frequent items in a stream of items for the recently proposed max-frequency measure . Based on the properties of an item , the max-frequency of an item is counted over a sliding window of which the length changes dynamically . Besides being parameterless , this way of measuring the support of items was shown to have the advantage of a faster detection of bursts in a stream , especially if the set of items is heterogeneous . The algorithm that was proposed for maintaining all frequent items , however , scales poorly when the number of items becomes large . Therefore , in this paper we propose , instead of reporting all frequent items , to only mine the top-k most frequent ones . First we prove that in order to solve this problem exactly , we still need a prohibitive amount of memory ( at least linear in the number of items ) . Yet , under some reasonable conditions , we show both theoretically and empirically that a memory-efficient algorithm exists . A prototype of this algorithm is implemented and we present its performance w.r.t. memory-efficiency on real-life data and in controlled experiments with synthetic data .
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