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A Comparison Study for Spectral, Ensemble and Spectral-Mean Shift Clustering Approaches for Interval-Valued Symbolic Data

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Analysis of Large and Complex Data

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Abstract

Interval-valued data arise in practical situations such as recording monthly interval temperatures at meteorological stations, daily interval stock prices, etc. This paper presents a comparison study for clustering efficiency (according to adjusted Rand index) for spectral, ensemble, and spectral-mean shifted clustering methods for symbolic data. Evaluation studies with application of artificial data with known cluster structure (obtained from mlbench and clusterSim packages of R) show the usefulness and stable results of the ensemble clustering compared to spectral and spectral-mean shift method.

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Authors and Affiliations

  1. Department of Econometrics and Computer Science, Faculty of Economics, Management and Tourism, Wrocław University of Economics, ul. Nowowiejska 3, 58-500, Jelenia Góra, Poland

    Marcin Pełka

Authors
  1. Marcin Pełka
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Correspondence to Marcin Pełka .

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Editors and Affiliations

  1. Jacobs University Bremen , Bremen, Germany

    Adalbert F.X. Wilhelm

  2. Universität Ulm, Institute of Medical Systems Biology Universität Ulm, Ulm, Baden-Württemberg, Germany

    Hans A. Kestler

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Pełka, M. (2016). A Comparison Study for Spectral, Ensemble and Spectral-Mean Shift Clustering Approaches for Interval-Valued Symbolic Data. In: Wilhelm, A., Kestler, H. (eds) Analysis of Large and Complex Data. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham. https://doi.org/10.1007/978-3-319-25226-1_12

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