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Data Mining pp 142–151Cite as

Adaptive Verfahren der Clusteranalyse und der multidimensionalen Skalierung für die Analyse und Visualisierung hochdimensionaler Datenmengen

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Part of the book series: Beiträge zur Wirtschaftsinformatik ((WIRTSCH.INFORM.,volume 27))

Zusammenfassung

Es wird ein Verfahren (ACMMDS=Adaptive C-Means and Multi-Dimensional Scaling) zur explorativen Datenanalyse vorgestellt. ACMMDS ist eine Kombination des adaptiven c-means Clusteranalyseverfahrens und eines multidimensionalen Skalierungsverfahrens. Es erlaubt die Visualisierung des Clusteranalyseprozesses ‚online ‘und kann als eine mögliche Alternative zu Kohonen’s selbstorganisierender Merkmalskarte (SOM) betrachtet werden. Während SOM ein heuristischer Algorithmus ist, kann ACMMDS aus bekannten Verfahren der multivariaten Statistik hergeleitet werden. Anhand von zwei verschiedenen Datensätzen wird die Anwendung von ACMMDS gezeigt.

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Literatur

  • Jain, A. and Dubes, R. (1988). Algorithms for Clustering Data. Prentice Hall, Englewood Cliffs, New Jersey.

    Google Scholar 

  • Kohonen, T. (1995). Self-Organizing Maps. Springer.

    Google Scholar 

  • Kushner, H. and Clark, D. (1978). Stochastic Approximation Methods for Constrained and Unconstrained Systems. Springer.

    Google Scholar 

  • Linde, Y., Buzo, A., and Gray, R. (1980). An algorithm for vector quantizer design. IEEE Transactions on Communications, 28(l):84–95.

    Article  Google Scholar 

  • Lloyd, S. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2): 129–137.

    Article  Google Scholar 

  • MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. In L.M.LeCam and J. Neyman, editors, Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, volume I, pages 281–297. Berkeley University of California Press.

    Google Scholar 

  • Moody, J. and Darken, C. (1989). Fast learning in networks of locally-tuned processing units. Neural Computation, 1(2):281–294.

    Article  Google Scholar 

  • Ritter, H. and Schulten, K. (1988). Convergence properties of Kohonen’s topology converving maps:fluctuations,stability, and dimension selection. Biological Cybernetics, 60:59–71.

    Article  Google Scholar 

  • Sammon, J. (1969). A nonlinear mapping for data structure analysis. IEEE Transactions on Computers, C-18:401–409.

    Article  Google Scholar 

  • Schalkoff, R. (1992). Pattern Recognition: Statistical, Structural and Neural Approaches. Wiley & Sons, New York.

    Google Scholar 

  • Schnell, R. (1994). Graphisch gestützte Datenanalyse. R. Oldenbourg Verlag, München Wien.

    Google Scholar 

  • Schwenker, F., Kestler, H., and Palm, G. (1996). Visualization and analysis of signal averaged high resolution electrocardiograms employing cluster analysis and multidimensional scaling. In IEEE International Conference on Computers in Cardiology, pages 453–456.

    Google Scholar 

  • Scott, D. (1992). Multivariate Density Estimation. John Wiley & Sons, New York.

    Book  Google Scholar 

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© 1998 Physica-Verlag Heidelberg

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Schwenker, F. (1998). Adaptive Verfahren der Clusteranalyse und der multidimensionalen Skalierung für die Analyse und Visualisierung hochdimensionaler Datenmengen. In: Nakhaeizadeh, G. (eds) Data Mining. Beiträge zur Wirtschaftsinformatik, vol 27. Physica-Verlag HD. https://doi.org/10.1007/978-3-642-86094-2_7

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  • DOI: https://doi.org/10.1007/978-3-642-86094-2_7

  • Publisher Name: Physica-Verlag HD

  • Print ISBN: 978-3-7908-1053-0

  • Online ISBN: 978-3-642-86094-2

  • eBook Packages: Springer Book Archive

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