... sensory data streams 知觉资料之流 Data Streams Mining 数据流挖掘 Evolving Data Streams 进化数据流 ...
基于8个网页-相关网页
General data-streams-mining federate 通用数据流挖掘成员
Mining of Data Streams 资料串流之探勘
Mining Massive Data Streams 挖掘大量数据流
How to make accurately outlier detection in real-time to data in these scenes and achieve the relevant application requirements has become a hot research topic in data streams mining.
如何对这些场景中大量的数据流实时准确地进行异常检测以达到相关的应用需求已成为当前数据流挖掘的研究热点。
参考来源 - 演化数据流的异常检测研究·2,447,543篇论文数据，部分数据来源于NoteExpress
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。
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