时间滑动窗口内基于密度的数据流聚类算法

为了提高数据流的聚类质量和效率,采用等时间跨度滑动窗口技术,然后利用改进的微簇结构保存数据流的概要信息,最后利用微簇删除策略,定期删除过期、孤立微簇。基于真实数据集与人工数据集的实验表明:与传统基于界标模型的聚类算法相比,该算法可获得较好的效率、较小的内存开销和快速的数据处理能力。

Density-based data stream clustering algorithm over time-based sliding windows

Stream data clustering algorithm was improved in terms of cluster quality and efficiency. This paper adopted a new method to improve cluster quality and efficiency. Firstly, the technology of the time-based sliding window was applied. Secondly, the structure of improved micro-cluster was created to save the summary. Finally, a new strategy was designed to regularly delete expired micro-clusters and outlier micro-clusters. Compared with traditional clustering algorithms of landmark-based model, the proposed method is of better efficiency, less memory overhead and fast data processing capabilities.