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Published in:
Modernising Official Statistics: A Complex Challenge Read chapter Read first chapter

2015 | OriginalPaper | Chapter

On-Line Clustering of Functional Boxplots for Monitoring Multiple Streaming Time Series

Authors : Elvira Romano, Antonio Balzanella

Published in: Data Science, Learning by Latent Structures, and Knowledge Discovery

Publisher: Springer Berlin Heidelberg

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Abstract

In this paper we introduce a micro-clustering strategy for functional boxplots. The aim is to summarize a set of streaming time series split in non-overlapping windows. It is a two-step strategy which performs at first, an on-line summarization by means of functional data structures, named Functional Boxplot micro-clusters; then, it reveals the final summarization by processing, off-line, the functional data structures. Our main contribute consists in providing a new definition of micro-cluster based on Functional Boxplots and in defining a proximity measure which allows to compare and update them. This allows to get a finer graphical summarization of the streaming time series by five functional basic statistics of data. The obtained synthesis will be able to keep track of the dynamic evolution of the multiple streams.

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previous chapter Resampling Techniques in Cluster Analysis: Is Subsampling Better Than Bootstrapping?
next chapter Smooth Tests of Fit for Gaussian Mixtures
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Metadata
Title
On-Line Clustering of Functional Boxplots for Monitoring Multiple Streaming Time Series
Authors
Elvira Romano
Antonio Balzanella
Copyright Year
2015
Publisher
Springer Berlin Heidelberg
Book
Data Science, Learning by Latent Structures, and Knowledge Discovery

Print ISBN: 978-3-662-44982-0

Electronic ISBN: 978-3-662-44983-7

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-662-44983-7_11

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