ST-CFSFDP:快速搜索密度峰值的时空聚类算法
时空聚类算法是地理时空大数据挖掘的基础研究命题。针对传统CFSFDP聚类算法无法应用于时空数据挖掘的问题，本文提出一种时空约束的ST-CFSFDP（spatial-temporal clustering by fast search and find of density peaks）算法。在CFSFDP算法基础上加入时间约束，修改了样本属性值的计算策略，不仅解决了原算法单簇集多密度峰值问题，且可以区分并识别相同位置不同时间的簇集。本文利用模拟时空数据与真实的室内定位轨迹数据进行对比试验。结果表明，该算法在时间阈值90 s、距离阈值5 m的识别正确率高达82.4%，较经典ST-DBCSAN、S...
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| Veröffentlicht in: | Ce hui xue bao 2019-11, Vol.48 (11), p.1380 |
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| container_title | Ce hui xue bao |
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| creator | 王培晓 张恒才 王海波 吴升 |
| description | 时空聚类算法是地理时空大数据挖掘的基础研究命题。针对传统CFSFDP聚类算法无法应用于时空数据挖掘的问题，本文提出一种时空约束的ST-CFSFDP（spatial-temporal clustering by fast search and find of density peaks）算法。在CFSFDP算法基础上加入时间约束，修改了样本属性值的计算策略，不仅解决了原算法单簇集多密度峰值问题，且可以区分并识别相同位置不同时间的簇集。本文利用模拟时空数据与真实的室内定位轨迹数据进行对比试验。结果表明，该算法在时间阈值90 s、距离阈值5 m的识别正确率高达82.4%，较经典ST-DBCSAN、ST-OPTICS及ST-AGNES聚类算法准确率分别提高了5.2%、4.2%和7.6%。 |
| doi_str_mv | 10.11947/j.AGCS.2019.20180538 |
| format | Article |
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| subjects | Algorithms Big Data Clustering Data mining Density Optics Searching Spatiotemporal data |
| title | ST-CFSFDP:快速搜索密度峰值的时空聚类算法 |
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