大数据下数据流聚类挖掘算法的优化分析

Internet of Things Technologies（2019）

Cited 1|Views12

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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Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

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