基于聚类的数据流挖掘技术的分析与研究

随着数据采集和通信技术的发展,对时时变化的不同来源的信息即数据流,实施实时监控将成为可能.数据流是大量的连续变化的数据序列,传输速度快,传统的挖掘算法将被适合于挖掘量大、能自由出入的数据流的技术所代替.笔者提出了用聚类技术来改进数据流挖掘的问题.笔者对K-均值算法、基于网格的统计聚类算法、回归分析算法等适用于数据流挖掘的算法进行了研究和分析,并对它们进行了比较.

Studying and analyzing on data streams mining technique based on clustering method

With the development of data gathering and communication technologies,it becomes increasingly possible to support real-time monitoring of large amount of information from diverse information sources.A data stream is a massive unbounded sequence of data elements continuously generated at a rapid rate.Due to this reason,traditional data mining approach is replaced by the system that is able to mine continuous,high-volume,open-ended data streams as they arrive.This paper introduces a new algorithm using clustering method to improve the data streams mining technique.We have studied clustering data streams using K-Means algorithm,statistical grid-based algorithm and regression analysis and compared these techniques.