频繁项集是挖掘流数据挖掘的基本任务。
Mining frequent items is a basic task in stream data mining.
目前流数据挖掘的主要挖掘模式是序列模式挖掘。
At present streaming data mining is the main mode of mining sequence pattern mining.
最后，分析了各个流数据挖掘任务中的代表性算法。
At last, the representative algorithms of every mining task are analyzed.
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。
Then the characters of stream data mining algorithms are summarized and several techniques that are used in these algorithms are introduced.
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 is a new research aspect of data mining. It has be come a useful tool for many fields.
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.
然后，在一个挖掘流中使用这个规则文件，把概念从文本列中提取到关系数据库表中。
We will then use this rule file in a mining flow to extract the concepts from text columns in relational database tables.
经理们掌握其进展情况，通过工具来提供导向使得从工作流中挖掘数据变得容易。
Managers stay abreast of their progress and provide direction by using tools that make it easy to mine data on workflows.
在挖掘网络世界一流的可视化数据方面，丘有着无可比拟的敏锐力。
Yau has an unerring ability to unearth the best data visualisations on the web.
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.
InfoSphere WarehouseDesignStudio是基于Eclipse的工具平台，用于为数据挖掘和文本分析设计工作负载规则、数据转换流和分析流。
InfoSphere Warehouse design Studio is the Eclipse-based tooling platform used to design workload rules, data transformation flows, and analytical flows for data mining and text analytics.
工作流挖掘的目标是：倒转过程，收集和利用运行数据，从而支持工作流设计和分析。
The goal of workflow mining is to reverse the process and collect data at runtime to support workflow design and analysis.
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.
学者们已提出大量处理流数据的挖掘算法。
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.
工作流挖掘的目标是：倒转过程，收集和利用运行数据，从而支持工作流设计和分析。
The goal of workflow mining is to reverse the process and collect the data at runtime to support workflow design and analysis.
是个开源的数据挖掘平台，通过一个用户友好的工作流接口提供通用数据挖掘模型的构建和数据清洗功能。
AlphaMiner is an open source data mining platform that offers versatile data mining model building and data cleansing features with an user friendly workflow interface.
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.
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。
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.
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.
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.
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.
从数据挖掘技术的角度研究了三江并流带地区丰富的旅游地质资源数据 ，采用“数据概化”的方法对资源数据集进行“维归约”预处理 。
Using the Web data mining technology makes full use of the advantages of cyber education, like resource sharing and no time limit.
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.
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.
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。
Those facts bring tremondous challenges to data-stream mining.
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