Journal of Computer Applications ›› 2011, Vol. 31 ›› Issue (05): 1363-1366.DOI: 10.3724/SP.J.1087.2011.01363
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LI Na, XING Chang-zheng
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李娜,邢长征
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Abstract: 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.
Key words: data stream, clustering, sliding window, micro cluster, landmark model
摘要: 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。
关键词: 数据流, 聚类, 滑动窗口, 微簇, 界标模型
LI Na XING Chang-zheng. Density-based data stream clustering algorithm over time-based sliding windows[J]. Journal of Computer Applications, 2011, 31(05): 1363-1366.
李娜 邢长征. 时间滑动窗口内基于密度的数据流聚类算法[J]. 计算机应用, 2011, 31(05): 1363-1366.
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URL: http://www.joca.cn/EN/10.3724/SP.J.1087.2011.01363
http://www.joca.cn/EN/Y2011/V31/I05/1363