大数据下数据流聚类挖掘算法的优化分析
Internet of Things Technologies(2019)
Abstract
随着IT技术的不断提升和完善,不管是在PC端,还是在移动端,人们借助互联网工具来实现的各种服务,都以数据的形式被记录下来,而这些数据不仅体量庞大、变化迅速,而且还呈现出一定的时序性.传统的数据分析已经不能适应如今庞大的数据流,同时不同的算法,最终所得到的处理结果也是不一样的,此时利用数据流相关的技术得到了重视和大规模的开发应用.鉴于此,文中通过明确数据流的概念和特点,并列举了常用的数据流聚类算法.充分考虑时间权值对数据流聚类的影响,在微簇中心点引入了时间衰减函数,提出F-Stream算法,分别对在线微聚类算法、离线宏聚类算法和数据流全局化缓存结构进行了优化设计.通过和CluStream算法进行时间效率、聚类质量和敏感参数的对比实验,发现F-Stream算法的整体性能更优,具有很好的聚类效果.
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