[1]陆剑锋,郭茂祖,张昱,等.基于时空约束密度聚类的停留点识别方法[J].智能系统学报,2020,15(1):59-66.[doi:10.11992/tis.201910026]
LU Jianfeng,GUO Maozu,ZHANG Yu,et al.Stay point recognition method based on spatio-temporal constraint density clustering[J].CAAI Transactions on Intelligent Systems,2020,15(1):59-66.[doi:10.11992/tis.201910026]
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LU Jianfeng,GUO Maozu,ZHANG Yu,et al.Stay point recognition method based on spatio-temporal constraint density clustering[J].CAAI Transactions on Intelligent Systems,2020,15(1):59-66.[doi:10.11992/tis.201910026]
基于时空约束密度聚类的停留点识别方法
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
15
期数:
2020年第1期
页码:
59-66
栏目:
学术论文—智能系统
出版日期:
2020-01-05
- Title:
- Stay point recognition method based on spatio-temporal constraint density clustering
- Keywords:
- stay point identification; density clustering; space-time constraint; indirect spatio-temporal feature; spatio-temporal similaily; aggregatied; process uniformity; fine-grained
- 分类号:
- TP301
- 摘要:
- 轨迹停留点的识别是轨迹分析、出行活动语义挖掘的关键。针对基于密度聚类的停留点识别方法对时空信息的表达缺陷，提出新的时空约束停留点识别方法，在密度聚类中引入轨迹的间接时空特征表示，将具有时空相似性的轨迹点进行聚合；采用与聚类过程相统一的时空特征约束对轨迹簇进行细粒度识别。算法在进行约束的时候再次利用到聚类时候所用的输入数据特征，特征的充分利用提高了识别的准确率。实验结果验证了本文方法的有效性。
- Abstract:
- The recognition of the track stay point is the key to the trajectory analysis and the semantic mining of travel activities. Aiming at the defect of spatio-temporal information based on density clustering, the new method of space-time constrained stay point recognition is proposed. In the density clustering, the indirect spatio-temporal feature representation of the trajectory is introduced, and the trajectory points with spatio-temporal similarity are aggregated. The spatio-temporal feature constraint unified with the clustering process is used to fine grain the trajectory cluster. Therefore, when the constraints are used, the input data features used in the clustering are reused, and the full utilization of the features improves accuracy of the recognition. The experimental results verify effectiveness of the proposed method.
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- 相似文献/References:
-
[1]周治平,王杰锋,朱书伟,等.一种改进的自适应快速AF-DBSCAN聚类算法[J].智能系统学报,2016,11(1):93.[doi:10.11992/tis.201410021]
ZHOU Zhiping,WANG Jiefeng,ZHU Shuwei,et al.An improved adaptive and fast AF-DBSCAN clustering algorithm[J].CAAI Transactions on Intelligent Systems,2016,11():93.[doi:10.11992/tis.201410021]
[2]郭茂祖,邵首飞,赵玲玲,等.基于时空周期模式挖掘的活动语义识别方法[J].智能系统学报,2021,16(1):162.[doi:10.11992/tis.202012035]
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备注/Memo
收稿日期:2019-10-31。
基金项目:国家自然科学基金项目(61871020，61502117，61305013)；北京市教委科技计划重点项目(KZ201810016019)；北京市属高校高水平创新团队建设计划项目(IDHT20190506)；国家重点研发计划项目(2016YFC0600901)
作者简介:陆剑锋，硕士研究生，主要研究方向为机器学习、智慧城市;郭茂祖，教授，博士生导师，主要研究方向为机器学习、智慧城市、生物信息学。主持和参与国家自然科学基金面上项目、北京市属高校高水平创新团队建设计划项目和北京市教委科技计划重点项目等。曾获得教育部高等学校科学研究优秀成果自然科学二等奖、省科技进步二等奖等。发表学术论文200余篇;赵玲玲，讲师，博士，主要研究方向为城市计算、生物信息学。主持和参与多项国家自然科学基金项目。发展学术论文50余篇。
通讯作者:赵玲玲.E-mail:zhaoll@hit.edu.cn
更新日期/Last Update:
1900-01-01