0


在象征性时间点和时间间隔数据中的时间模式挖掘

Temporal Pattern Mining in Symbolic Time Point and Time Interval Data
课程网址: http://videolectures.net/kdd2010_moerchen_tpm/  
主讲教师: Fabian Moerchen
开课单位: 西门子公司
开课时间: 2010-10-01
课程语种: 英语
中文简介:
我们提出了一个统一的时间概念和数据模型的观点,以便从符号时间数据中分类现有的无监督模式挖掘方法。我们区分了基于时间点的方法和基于区间的方法,以及单变量和多变量的方法。对于每个主要类别,我们都介绍了最重要的算法。对于时间点,可以使用顺序模式挖掘算法来表示多变量数据中存在间隙的时间点的相等性和顺序。近年来,人们提出了从时间点挖掘偏序概念的有效算法。对于具有精确起点和终点的时间间隔数据,可以使用Allen的关系来制定模式。提出了几种备选方案和扩展方案。我们进一步将读者引向时间数据挖掘的预处理方法。
课程简介: We present a unifying view of temporal concepts and data models in order to categorize existing approaches for unsupervised pattern mining from symbolic temporal data. We distinguish time point-based methods and interval-based methods as well as univariate and multivariate methods. For each of the main categories we present the most important algorithms. For time points, sequential pattern mining algorithms can be used to express equality and order of time points with gaps in multivariate data. Recently, efficient algorithms have been proposed to mine the more general concept of partial order from time points. For time interval data with precise start and end points the relations of Allen can be used to formulate patterns. Several alternatives and extensions have been proposed. We further point the audience to preprocessing methods from temporal data mining.
关 键 词: 时间概念; 数据模型; 时间间隔数据; 多变量数据
课程来源: 视频讲座网
最后编审: 2020-06-15:wuyq
阅读次数: 57