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。
Frequent pattern mining is one basic research of data stream mining.
数据流频繁模式挖掘是数据流挖掘的基础研究之一。
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。
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。
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。
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。
应用推荐