Title :
Mining Frequent Items Based on Bloom Filter
Author :
Wang, Shuyun ; Hao, Xiulan ; Xu, Hexiang ; Hu, Yunfa
Author_Institution :
Fudan Univ., Shanghai
Abstract :
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.
Keywords :
data mining; bloom filter; damped model; data streams; data structure; Counting circuits; Data mining; Data structures; Fading; Filters; Frequency estimation; Frequency shift keying; Information filtering; Monitoring; Sampling methods;
Conference_Titel :
Fuzzy Systems and Knowledge Discovery, 2007. FSKD 2007. Fourth International Conference on
Conference_Location :
Haikou
Print_ISBN :
978-0-7695-2874-8
DOI :
10.1109/FSKD.2007.400
