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Data Mining Approach Based on Information-Statistical Analysis: Application to Temporal-Spatial Data

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Machine Learning and Data Mining in Pattern Recognition (MLDM 2001)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2123))

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Abstract

An information-statistical approach is proposed for analyzing temporal-spatial data. The basic idea is to analyze the temporal aspect of the data by first conditioning on specific spatial nature of the data. Parametric approach based on Guassian model is employed for analyzing the temporal behavior of the data. Schwarz information criterion is then applied to detect multiple mean change points --- thus the Gaussian statistical models - to account for changes of the population mean over time. To examine the spatial characteristics of the data, successive mean change points are qualified by finite categorical values. The distribution of the finite categorical values is then used to estimate a non-parametric probability model through a non-linear SVD-based optimization approach; where the optimization criterion is Shannon expected entropy. This optimal probability model accounts for the spatial characteristics of the data and is then used to derive spatial association patterns subject to chi-square statistic hypothesis test.

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Author information

Authors and Affiliations

  1. Computer Science Department, Queens College/CUNY, Flushing, NY, 11367, USA

    Bon K. Sy

  2. Department of Mathematics and Statistics, Bowling Green State University, BowlingGreen, Ohio, 43403, USA

    Arjun K. Gupta

Authors
  1. Bon K. Sy
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  2. Arjun K. Gupta
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Editor information

Editors and Affiliations

  1. Institute of Computer Vision and Applied Computer Sciences, Arno-Nitzsche-Str. 45, 04277, Leipzig, Germany

    Petra Perner

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© 2001 Springer-Verlag Berlin Heidelberg

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Sy, B.K., Gupta, A.K. (2001). Data Mining Approach Based on Information-Statistical Analysis: Application to Temporal-Spatial Data. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2001. Lecture Notes in Computer Science(), vol 2123. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44596-X_11

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  • DOI: https://doi.org/10.1007/3-540-44596-X_11

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42359-1

  • Online ISBN: 978-3-540-44596-8

  • eBook Packages: Springer Book Archive

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