Mining Frequent Items Based on Bloom Filter
Shuyun Wang, Xiulan Hao, Hexiang Xu, Yunfa Hu · 2007
This paper introduce the algorithm MIBFD (mining frequent items using bloom filter based on damped model) for mining recent frequent items in data streams. Based on an efficient data structure named extensible and scalable bloom filter(ESBF), MIBFD is able to adjust the size of memory used dynamically. Theoretical analysis and experiments show that MIBFD is efficient both in processing time and in memory usage.