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We investigate the problem of finding frequent items in a continuous data stream, and present an algorithm named λ-HCount for computing frequency counts of stream data based on a time fading model. The algorithm uses r hash functions to estimate the density values of stream data items. To emphasize the importance of recent data items, a time fading factor is used. For a given error bound, our algorithm...
Frequent items mining is an important data mining task with many real-world applications. By considering different weights of the items, weighted frequent items mining can discover more important knowledge compared to traditional frequent patterns mining. In this paper, we presented a new algorithm called count-MH to discover weighted frequent items over data streams, the proposed method is based...
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