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A Two-Phase Clustering Based Strategy for Outliers Detection in Georeferenced Curves

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Statistical Models for Data Analysis

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Abstract

A two-phase clustering method for the detection of geostatistical functional outliers is proposed. It first, clusters data by a modified version of a Dynamic Clustering algorithm for geostatical functional data and then detects groups of outliers according to a cut-off value defined by a measure of spatial deviation in a minimum spanning tree. The performance of the proposed procedure is analyzed by several simulation studies.

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Authors and Affiliations

  1. Second University of Naples, Caserta, Italy

    Elvira Romano & Antonio Balzanella

Authors
  1. Elvira Romano
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  2. Antonio Balzanella
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Corresponding author

Correspondence to Elvira Romano .

Editor information

Editors and Affiliations

  1. Department of Economics, and Management, University of Pavia, Via San Felice 7, Pavia, 27100, Italy

    Paolo Giudici

  2. Department of Economics, and Business, University of Catania, Corso Italia 55, Catania, 95129, Italy

    Salvatore Ingrassia

  3. , Department of Statistics, University of Rome "La Sapienza", Piazzale Aldo Moro 5, Rome, 00185, Italy

    Maurizio Vichi

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Romano, E., Balzanella, A. (2013). A Two-Phase Clustering Based Strategy for Outliers Detection in Georeferenced Curves. In: Giudici, P., Ingrassia, S., Vichi, M. (eds) Statistical Models for Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-00032-9_34

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