频繁项挖掘的优化办法 FIMI Workshop
The lossy counting algorithm is such a classical frequent item mining algorithm.
Lossy Counting算法是经典的数据流频繁项挖掘算法。
参考来源 - 网络信息流中热门访问主题挖掘技术研究·2,447,543篇论文数据，部分数据来源于NoteExpress
许多近似算法能够有效进行频繁项挖掘，但不能有效控制内存资源消耗。
Many approximation algorithms behave well in frequent items mining, but can not control their memory consumption.
数据流频繁项挖掘算法需要利用有限的内存，以尽量少的次数扫描数据流就能得到频繁项。
Frequent item mining algorithms need to perform as little data stream scanning as possible while using limited size of memory.
提出了一种流数据上的频繁项挖掘算法(SW - COUNT)。该算法通过数据采样技术挖掘滑动窗口下的数据流频繁项。
A frequent items mining algorithm of stream data (SW-COUNT) was proposed, which used data sampling technique to mine frequent items of data flow under sliding Windows.
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