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Unsupervised pattern mining from symbolic temporal data

Published: 01 June 2007 Publication History

Abstract

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. In particular we distinguish time point-based methods and interval-based methods as well as univariate and multivariate methods. The mining paradigms and the robustness of many proposed approaches are compared to aid the selection of the appropriate method for a given problem. For time points, sequential pattern mining algorithms can be used to express equality and order of time points with gaps in multivariate data. For univariate data and limited gaps suffix tree methods are more efficient. 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. The recently proposed Time Series Knowledge Representation is more robust on noisy data and offers an alternative semantic that avoids ambiguity and is more expressive. For both pattern languages efficient mining algorithms have been proposed.

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cover image ACM SIGKDD Explorations Newsletter
ACM SIGKDD Explorations Newsletter  Volume 9, Issue 1
Special issue on data mining for health informatics
June 2007
58 pages
ISSN:1931-0145
EISSN:1931-0153
DOI:10.1145/1294301
Issue’s Table of Contents

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 June 2007
Published in SIGKDD Volume 9, Issue 1

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