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。
- $textbf{Trajectory Data}$:主要分为四个类别
- $texttt{Mobility of people}$
- $texttt{Mobility of transportation}$
- $texttt{Mobility of animals}$
- $texttt{Mobility of natural phenomena}$
- $textbf{Trajectory Data Preprocessing}$
- $texttt{Noise Filtering}$
- $textit{Mean Filter}$
- $textit{Kalman and Particle Filters}$
- $textit{Heuristics-Based Outlier Detection}$
- $texttt{Stay Point Detection}$
- $texttt{Trajectory Compression}$:对轨迹数据进行压缩,以减少计算量
- $textit{Distance Metric}$
- $textit{Offline Compression}$
- $textit{Online Data Reduction}$
- $textit{Compression with Semantic Meaning}$
- $texttt{Trajectory Segmentation}$:对轨迹数据进行切割
- $textit{time interval}$
- $textit{shape of a trajectory}$
- $textit{semantic meanings}$
- $texttt{Map Matching}$:对原始的经纬度数据转化为路网数据
- $textit{geometric}$
- $textit{topological}$
- $textit{probabilis 大专栏 VI.应用-Trajectory Data Miningtic}$
- $textit{other advanced techniques}$
- $texttt{Noise Filtering}$
- $textbf{Trajectory Data Management}$
- $texttt{Trajectory Indexing and Retrieval}$:没看懂是为了解决什么问题
- $texttt{Distance/Similarity of Trajectories}$:了解一下度量方式
- $textbf{Uncertainty in Trajectory Data}$
- $texttt{Reducing Uncertainty from Trajectory Data}$:解决因采样率低,造成数据稀疏,不确定性增大等问题
- $textit{Modeling Uncertainty of a Trajectory for Queries}$
- $textit{Path Inference from Uncertain Trajectories}$
- $texttt{Privacy of Trajectory Data}$:为保护隐私性,需要增大数据的不确定性。
- $texttt{Reducing Uncertainty from Trajectory Data}$:解决因采样率低,造成数据稀疏,不确定性增大等问题
- $textbf{Trajectory Pattern Mining}$
- $texttt{Moving Together Patterns}$
- $texttt{Trajectory Clustering}$
- $texttt{Mining Sequential Patterns from Trajectories}$
- $texttt{Mining Periodical Patterns from Trajectories
}$
- $textbf{Trajectory Classification}$:做运动状态分类、交通方式分类等分类任务
- $textbf{Anomalies Detection From Trajectories}$
- $texttt{Detecting Outlier Trajectories}$
- $texttt{Identifying Anomalous Events by Trajectories}$
- $textbf{Transfer Trajectory To Other Representations}$
- $texttt{From Trajectory to Graph}$
- $texttt{From Trajectory to Matrix}$
- $texttt{From Trajectory to Tensor}$
VI.应用-Trajectory Data Mining的更多相关文章
- Distributed Databases and Data Mining: Class timetable
Course textbooks Text 1: M. T. Oszu and P. Valduriez, Principles of Distributed Database Systems, 2n ...
- What is the most common software of data mining? (整理中)
What is the most common software of data mining? 1 Orange? 2 Weka? 3 Apache mahout? 4 Rapidminer? 5 ...
- What’s the difference between data mining and data warehousing?
Data mining is the process of finding patterns in a given data set. These patterns can often provide ...
- A web crawler design for data mining
Abstract The content of the web has increasingly become a focus for academic research. Computer prog ...
- Datasets for Data Mining and Data Science
https://github.com/mattbane/RecommenderSystem http://grouplens.org/datasets/movielens/ KDDCUP-2012官网 ...
- cluster analysis in data mining
https://en.wikipedia.org/wiki/K-means_clustering k-means clustering is a method of vector quantizati ...
- Weka 3: Data Mining Software in Java
官方网站: Weka 3: Data Mining Software in Java 相关使用方法博客 WEKA使用教程(经典教程转载) (实例数据:bank-data.csv) Weka初步一.二. ...
- data mining,machine learning,AI,data science,data science,business analytics
数据挖掘(data mining),机器学习(machine learning),和人工智能(AI)的区别是什么? 数据科学(data science)和商业分析(business analytics ...
- 数据挖掘(data mining),机器学习(machine learning),和人工智能(AI)的区别是什么? 数据科学(data science)和商业分析(business analytics)之间有什么关系?
本来我以为不需要解释这个问题的,到底数据挖掘(data mining),机器学习(machine learning),和人工智能(AI)有什么区别,但是前几天因为有个学弟问我,我想了想发现我竟然也回答 ...
随机推荐
- 吴裕雄--天生自然 PYTHON3开发学习:迭代器与生成器
list=[1,2,3,4] it = iter(list) # 创建迭代器对象 for x in it: print (x, end=" ") import sys # 引入 s ...
- Rikka with Prefix Sum
Rikka with Prefix Sum 题目 https://www.nowcoder.com/acm/contest/148/D 题目有三个操作 l到r都添加一个数 取一次前缀和 查询区间和 这 ...
- log4j中%5p的含义
因为日志级别分别有error,warn,info,debug,fatal5种,有些是5个字母的,有些是4个字母的,如果直接写%p就会对不齐,%-5p的意思是日志级别输出左对齐,右边以空格填充,%5p的 ...
- 华为路由器AR1220E-S通过web页面不能登录
问题原因:由于在WEB页面配置了“远程信任主机”,但是信任主机和路由器不在一个网段,导致所有IP都不能通过WEB页面管理路由器 解决方案:通过console口直接连接路由器,删除信任主机,此次咨询了华 ...
- 吴裕雄--天生自然python机器学习:朴素贝叶斯算法
分类器有时会产生错误结果,这时可以要求分类器给出一个最优的类别猜测结果,同 时给出这个猜测的概率估计值. 概率论是许多机器学习算法的基础 在计算 特征值取某个值的概率时涉及了一些概率知识,在那里我们先 ...
- LeetCode No.97,98,99
No.97 IsInterleave 交错字符串 题目 给定三个字符串 s1, s2, s3, 验证 s3 是否是由 s1 和 s2 交错组成的. 示例 输入: s1 = "aabcc&qu ...
- 怎么保证RabbitMQ和kafuka集群的高可用性?
rabbitMQ有三种模式:单机模式,普通集群模式,镜像集群模式 RabbitMQ的高可用性 RabbitMQ是比较有代表性的,因为是基于主从做高可用性的,我们就以他为例子讲解第一种MQ的高可用性 ...
- Spring Cloud服务的注册与发现(Eureka)
一.spring cloud简介 spring cloud 为开发人员提供了快速构建分布式系统的一些工具,包括配置管理.服务发现.断路器.路由.微代理.事件总线.全局锁.决策竞选.分布式会话等等.它运 ...
- input之按键输入
通过直接操作驱动来监控键盘,只要程序一旦在后台启动,无论在任何页面都可以监控到按键的数值. 步骤如下: 1.找到键盘挂在点:有两种方法 方法一:在 /dev/input路径下通过 cat eve ...
- 2019牛客多校(第十场)F Popping Balloons —— 线段树+枚举
https://ac.nowcoder.com/acm/contest/890/F 题意:二维平面中有n个气球,你可以横着社三法子弹,竖着射三发子弹,且横着子弹的关系是y,y+r,y+2*r,竖着是x ...