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Temporal Pattern Mining in Symbolic Time Point and Time Interval Data
This video was recorded at 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), Washington 2010. 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.
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This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States
