Mining most frequent K items in data streams means finding K items whose frequencies are larger than other items in data streams.
数据流最频繁K项挖掘是指在数据流中找出K个项，它们的支持数大于数据流中的其他项。
Data streams are continuous, fast, unlimited, unknown, so traditional technology of data mining is not suitable to data stream mining. Analysis and mining data stream has been a popular research.
数据流的连续、快速、无限、未知的特点决定了传统的数据挖掘技术已经不适合数据流挖掘，分析和挖掘数据流已经成为热点研究问题。
Traditional data mining algorithms aiming at static datasets can't be used to mine data streams directly, neither do they have the time and space efficiency.
传统面向静态数据集的算法无法直接用于挖掘数据流，而现有数据流挖掘算法存在时空效率不高的缺陷。
Concept drift, as a difficult point in the field of data stream mining, is generated with the accompany of time-varying data streams.
概念漂移是数据流分类挖掘中的一个难点，它是伴随着数据流的时变性而产生的。
The tracking of drifting concept from data streams has recently become one of hot spots in data mining.
数据流上的漂移概念发现已成为数据挖掘领域的研究热点之一。
By analyzing data streams outliers mining situation of foreign and domain, we found that there exist many problems in the previous algorithms for detecting outliers.
对国内外数据流离群数据挖掘研究情况分析可知，以往的挖掘算法还存在诸多问题。
By analyzing data streams outliers mining situation of foreign and domain, we found that there exist many problems in the previous algorithms for detecting outliers.
对国内外数据流离群数据挖掘研究情况分析可知，以往的挖掘算法还存在诸多问题。
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