zoukankan      html  css  js  c++  java
  • VI.应用-Trajectory Data Mining

    $textbf{Trajectory Data Mining: An Overview}$

    很好的一篇概述,清晰明了地阐述了其框架,涉及内容又十分宽泛。值得细读。

    未完成,需要补充。

    1. $textbf{Trajectory Data}$:主要分为四个类别
      1. $texttt{Mobility of people}$
      2. $texttt{Mobility of transportation}$
      3. $texttt{Mobility of animals}$
      4. $texttt{Mobility of natural phenomena}$
    2. $textbf{Trajectory Data Preprocessing}$
      1. $texttt{Noise Filtering}$
        1. $textit{Mean Filter}$
        2. $textit{Kalman and Particle Filters}$
        3. $textit{Heuristics-Based Outlier Detection}$
      2. $texttt{Stay Point Detection}$
      3. $texttt{Trajectory Compression}$:对轨迹数据进行压缩,以减少计算量
        1. $textit{Distance Metric}$
        2. $textit{Offline Compression}$
        3. $textit{Online Data Reduction}$
        4. $textit{Compression with Semantic Meaning}$
      4. $texttt{Trajectory Segmentation}$:对轨迹数据进行切割
        1. $textit{time interval}$
        2. $textit{shape of a trajectory}$
        3. $textit{semantic meanings}$
      5. $texttt{Map Matching}$:对原始的经纬度数据转化为路网数据
        1. $textit{geometric}$
        2. $textit{topological}$
        3. $textit{probabilis 大专栏  VI.应用-Trajectory Data Miningtic}$
        4. $textit{other advanced techniques}$
    3. $textbf{Trajectory Data Management}$
      1. $texttt{Trajectory Indexing and Retrieval}$:没看懂是为了解决什么问题
      2. $texttt{Distance/Similarity of Trajectories}$:了解一下度量方式
    4. $textbf{Uncertainty in Trajectory Data}$
      1. $texttt{Reducing Uncertainty from Trajectory Data}$:解决因采样率低,造成数据稀疏,不确定性增大等问题
        1. $textit{Modeling Uncertainty of a Trajectory for Queries}$
        2. $textit{Path Inference from Uncertain Trajectories}$
      2. $texttt{Privacy of Trajectory Data}$:为保护隐私性,需要增大数据的不确定性。
    5. $textbf{Trajectory Pattern Mining}$
      1. $texttt{Moving Together Patterns}$
      2. $texttt{Trajectory Clustering}$
      3. $texttt{Mining Sequential Patterns from Trajectories}$
      4. $texttt{Mining Periodical Patterns from Trajectories
        }$
    6. $textbf{Trajectory Classification}$:做运动状态分类、交通方式分类等分类任务
    7. $textbf{Anomalies Detection From Trajectories}$
      1. $texttt{Detecting Outlier Trajectories}$
      2. $texttt{Identifying Anomalous Events by Trajectories}$
    8. $textbf{Transfer Trajectory To Other Representations}$
      1. $texttt{From Trajectory to Graph}$
      2. $texttt{From Trajectory to Matrix}$
      3. $texttt{From Trajectory to Tensor}$
  • 相关阅读:
    集训队作业2018人类的本质
    推式子小技巧
    [Codeforces671D]Roads in Yusland
    线性规划的对偶问题
    数学虐哭空巢老人记
    Voronoi图与Delaunay三角剖分
    [ZJOI2018]保镖
    [SPOJ2939]Qtree5
    数据结构虐哭空巢老人记
    [CTSC2006]歌唱王国
  • 原文地址:https://www.cnblogs.com/lijianming180/p/12262603.html
Copyright © 2011-2022 走看看