A Load Shedding Scheme for Frequent Pattern Mining in Transactional Data Streams
In this paper, we study overload handling for frequent-pattern mining in online data streams. For a mining system with an ?-deficient synopsis based algorithm, we propose a load shedding scheme to deal with the overload situation. The heavy workload of the mining algorithm lies mostly in the great deal of itemsets which need to be enumerated and counted by the mining algorithm. Therefore, our proposed scheme of load shedding involves the maintenance of a smaller set of itemsets, so the workload can be lessened accordingly. The unrecorded itemsets can be fast approximated for their counts when necessary. According to experimental results, the load shedding scheme can increase the throughput of the mining system and thus help manage the overload problem effectively to a certain extent.
data mining data stream frequent itemset data overload loadshedding
Kuen-Fang Jea Chao-Wei Li Chih-Wei Hsu Ru-Ping Lin Ssu-Fan Yen
Department of Computer Science and Engineering National Chung-Hsing University Taichung 40227, Taiwan, R.O.C.
国际会议
上海
英文
1347-1352
2011-07-26（万方平台首次上网日期，不代表论文的发表时间）