Data stream mining is a new research aspect of data mining. It has be come a useful tool for many fields.
流数据挖掘是数据挖掘的一个新的研究方向，已逐渐成为许多领域的有用工具。
Data stream mining has attracted many researchers 'attention and has become a useful tool for many fields.
近年来，数据流挖掘越来越引起研究人员的关注，已逐渐成为许多领域有用的工具。
Absrtact: Data stream mining has attracted many researchers 'attention and has become a useful tool for many fields.
摘要：近年来，数据流挖掘越来越引起研究人员的关注，已逐渐成为许多领域有用的工具。
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。
This paper describes the relevant concepts and presents a model of CBR based on dynamic data stream mining, and gives an improved clustering algorithm of data stream.
首先阐述了相关概念，接着提出了一种基于动态数据流挖掘的案例推理模型，其中动态数据流挖掘算法采用改进的数据流聚类算法。
Thus, it is important to research data stream mining algorithms having higher time and space efficiency, and to aim at resolving data mining tasks often used in system simulation.
因此，针对仿真中常用的数据挖掘任务，研究时空效率高效的相应数据流挖掘算法具有重要意义。
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。
Mining frequent items is a basic task in stream data mining.
频繁项集是挖掘流数据挖掘的基本任务。
Then the characters of stream data mining algorithms are summarized and several techniques that are used in these algorithms are introduced.
然后，总结了流数据挖掘算法的特点，并给出了算法中常用的技术。
Many new techniques and methods on frequent pattern mining in data stream have been proposed.
国内外学者已提出许多新的挖掘数据流频繁模式的方法和技术。
With occurrences of many related applications, recently frequent episodes mining in event stream has become a hot research topic in data mining field.
在事件流上挖掘频繁片断已经成为近来研究的热点，在很多应用中起到重要作用。
Some characters of Data stream make that static mining method can't meet the requirements of nowadays mining application.
数据流本身的特点使得静态挖掘方法不再满足要求。
Finally the problems in applying the MADSPM model to association rule mining in stream data are discussed and the strategies for solving them are also given.
最后对基于MADSPM模型的流数据关联规则挖掘问题中需注意的一些问题进行了阐述与分析。
The traditional algorithm of mining outliers cannot mine outliers in data stream effectively.
传统的离群点挖掘算法无法有效挖掘数据流中的离群点。
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.
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。
In addition, the business data stream is continuous, conflict, timing, massive and distributed, so traditional data mining techniques can not be applied directly to the business data stream.
而商业数据流除了具备数据流的基本特点外，还具备连续性、冲突性、时间性、海量性和分布性等特性。因此传统的数据挖掘技术不能直接应用到商业数据流上。
In Stream Model of Industry, The application of Data Mining must bring higher product yield and quality, and create more benefit.
在流程型工业中，数据挖掘的应用必定会给工业带来更高的产品产量与质量，以及创造更大的效益。
Present a system framework of intelligent DSS based on click-stream data mining technology.
提出基于点击流挖掘技术的商务智能辅助决策系统框架。
Frequent item mining algorithms need to perform as little data stream scanning as possible while using limited size of memory.
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。
Those facts bring tremondous challenges to data-stream mining.
数据流的特征对数据流的挖掘提出了严峻的挑战。
Those facts bring tremondous challenges to data-stream mining.
数据流的特征对数据流的挖掘提出了严峻的挑战。
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