数据流频繁模式挖掘是数据流挖掘的基础研究之一。
Frequent pattern mining is one basic research of data stream mining.
最后，举例说明了数据流挖掘的应用，并展望了数据流挖掘未来的研究方向。
Finally, main applications and future research directions of data stream mining are pu...
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。
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。
Absrtact: Data stream mining has attracted many researchers 'attention and has become a useful tool for many fields.
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。
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.
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。
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.
研究数据流上的历史数据的变化趋势，并预测数据流在未来时间窗口内的可能值是数据流挖掘的一项重要工作。
It is also an important work to study the varying tendency of the historic stream data ina data stream and predict the possible values of the stream data in the future time window.
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。
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.
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。
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.
通过此模型使用基于动态数据流挖掘的案例推理技术，对数据进行实时挖掘，产生连续、动态的临时案例库，实现知识库的实时更新，从而满足实际问题变化的需要。
Through this model the system can mine real-time datum, produce continuous, dynamic temporary cases, update the knowledge base in real time and meet the needs of the practical problems.
查看InfoSphereWarehouse文档了解如何查看挖掘流或数据流的uima日志，以及如果更改uima跟踪级别获得更多信息。
See the InfoSphere Warehouse documentation for instructions on how to see the UIMA logs of a mining or data flow and how to change the UIMA trace level to get more information.
如果您对流程、数据流或挖掘流启用内容跟踪，那么CONFIG级别和该级别以上的UIMA日志将路由到InfoSphereWarehousing日志。
If you enable content tracing for a process, a data flow, or a mining flow, UIMA logs of level CONFIG and above are routed to the InfoSphere Warehousing log.
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。
Many new techniques and methods on frequent pattern mining in data stream have been proposed.
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。
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.
传统的离群点挖掘算法无法有效挖掘数据流中的离群点。
The traditional algorithm of mining outliers cannot mine outliers in data stream effectively.
频繁项集挖掘是一个非常基本的，但最重要的任务，在数据流处理。
Frequent items mining is a very basic but important task in the data stream processing.
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.
数据流本身的特点使得静态挖掘方法不再满足要求。
Some characters of Data stream make that static mining method can't meet the requirements of nowadays mining application.
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.
针对数据流的无限输入和动态变化等特点，提出一种新的基于距离的数据流离群点挖掘算法。
Concerning the infinite input and dynamic change in data stream environment, a new algorithm for detecting data stream outliers based on distance was proposed.
首先将流数据与传统数据进行比较，然后对数据流的聚类分析，频繁集挖掘作了介绍。
Compared stream data with traditional data, this paper is in order to mine frequency items, clustering over data stream.
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。
Concept drift, as a difficult point in the field of data stream mining, is generated with the accompany of time-varying data streams.
针对频繁闭项集挖掘算法中数据结构与处理机制复杂的问题，提出窗口快速滑动的数据流频繁闭项集挖掘算法——MFWSR。
This paper proposes an algorithm of Mining Frequent closed itemsets with Window Sliding Rapidly(MFWSR) against the complexity of data structure and process for determination.
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 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 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.
应用推荐