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Time-Frequency Filtering for Seismic Waves Clustering Read chapter Read first chapter

2014 | OriginalPaper | Chapter

A Box-Plot and Outliers Detection Proposal for Histogram Data: New Tools for Data Stream Analysis

Authors : Rosanna Verde, Antonio Irpino, Lidia Rivoli

Published in: Analysis and Modeling of Complex Data in Behavioral and Social Sciences

Publisher: Springer International Publishing

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Abstract

In this paper, we propose a method for monitoring the evolution of data described by histograms of values. Our proposal consists to define new order statistics on the quantile functions associated with the empirical distributions, represented by the histogram-data. We introduce the Median, the First and the Third Quartile quantile functions, as well as a generalized representation of the box and whiskers plot. For example, the proposed representations and indices are useful for identifying and classifying outliers, arriving along the time in a data stream environment.

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previous chapter Robustness and Stability Analysis of Factor PD-Clustering on Large Social Data Sets
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Literature
Arroyo, J., González-Riviera, G., Maté, C., & Muñoz San Roque, A. (2011). Smoothing methods for histogram-valued time series. An application to value-at-risk. Statistical Analysis and Data Mining, 4(2), 216–228.CrossRefMathSciNet
Gama, J., & Pinto, C. (2006). Discretization from data streams: Applications to histograms and data mining. In Proceedings of the ACM Symposium on Applied Computing (pp. 662–667), New York.
Gilchris, W. (2000). Statistical modelling with quantile functions. London/Boca Raton: Chapman & Hall/CRC.CrossRef
Irpino, A., & Verde, R. (2006). Dynamic clustering of histograms using Wasserstein metric. In A. Rizzi & M. Vichi (Eds.), Advances in computational statistics (pp. 869–876). Heidelberg: Physica-Verlag.
Rivoli, L., Irpino, A., & Verde, R. (2012). The median of a set of histogram data. In XLVI Riunione Scientifica della Società Italiana di Statistica, CLEUP [ISBN 978-88-6129-882-8].
Verde, R., & Irpino, A. (2007). Dynamic clustering of histogram data: Using the right metric. In Studies in classification, data analysis, and knowledge organization (vol. I, pp. 123–134).
Verde, R., & Irpino, A. (2008). Comparing histogram data using a Mahalanobis-Wasserstein distance (COMPSTAT 2008) (pp. 77–89). Heidelberg: Physica-Verlag.
Tukey, J. W. (1977). Exploratory data analysis. Reading: Addison-Wesley.MATH
Metadata
Title
A Box-Plot and Outliers Detection Proposal for Histogram Data: New Tools for Data Stream Analysis
Authors
Rosanna Verde
Antonio Irpino
Lidia Rivoli
Copyright Year
2014
Book
Analysis and Modeling of Complex Data in Behavioral and Social Sciences

Print ISBN: 978-3-319-06691-2

Electronic ISBN: 978-3-319-06692-9

Copyright Year: 2014

DOI
https://doi.org/10.1007/978-3-319-06692-9_30

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