Using Bloom Filters for Mining Top-k Frequent Itemsets in Data Streams
作者： Younghee Kim , Kyungsoo Cho , Jaeyeol Yoon , Ieejoon Kim , Ungmo Kim
DOI: 10.1007/978-3-642-22339-6_25
关键词:
摘要: In this paper, we study the problem of finding top-k most frequent itemsets in data streams. To only mine restricted to sub-domains workspace or result some query. Most previous algorithms are clearly not suitable for with limited memory, such as instance, an allocated each stream summary. Therefore, propose that order solve memory efficiency mining from massively and speedy a stream. Our algorithm is used bloom filter structure, named MineTop-k, which permit efficient computation maintenance results. We show our approach memory-efficient method problem.
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sci-hub.st 参考文章(13)
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