计算机科学 ›› 2018, Vol. 45 ›› Issue (1): 97-102.doi: 10.11896/j.issn.1002-137X.2018.01.015
• CRSSC-CWI-CGrC-3WD 2017 • 上一篇 下一篇
瞿原,邓维斌,胡峰,张其龙,王鸿
QU Yuan, DENG Wei-bin, HU Feng, ZHNG Qi-long and WANG Hong
摘要： 点排序识别聚类结构(Ordering Points to Identify the Clustering Structure,OPTICS)的密度聚类算法能以可视化的方式导出数据集的内在聚类结构,并且可以通过簇排序提取基本的聚类信息。但是该算法由于时空复杂度较高,不能很好地适应当今社会出现的大型数据集。随着云计算和并行计算的发展,提供了一种解决OPTICS算法复杂度缺陷的方法和一种建立在基于Spark内存计算平台的点排序识别聚类结构并行算法。测试的实验结果表明,它能极大地降低OPTICS算法对时间和空间的需要。
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