article

Leveraging the power of local spatial autocorrelation in geophysical interpolative clustering

Authors:

Annalisa Appice,

Donato MalerbaAuthors Info & Claims

Data Mining and Knowledge Discovery, Volume 28, Issue 5-6

Pages 1266 - 1313

https://doi.org/10.1007/s10618-014-0372-z

Published: 01 September 2014 Publication History

Abstract

Nowadays ubiquitous sensor stations are deployed worldwide, in order to measure several geophysical variables (e.g. temperature, humidity, light) for a growing number of ecological and industrial processes. Although these variables are, in general, measured over large zones and long (potentially unbounded) periods of time, stations cannot cover any space location. On the other hand, due to their huge volume, data produced cannot be entirely recorded for future analysis. In this scenario, summarization, i.e. the computation of aggregates of data, can be used to reduce the amount of produced data stored on the disk, while interpolation, i.e. the estimation of unknown data in each location of interest, can be used to supplement station records. We illustrate a novel data mining solution, named interpolative clustering, that has the merit of addressing both these tasks in time-evolving, multivariate geophysical applications. It yields a time-evolving clustering model, in order to summarize geophysical data and computes a weighted linear combination of cluster prototypes, in order to predict data. Clustering is done by accounting for the local presence of the spatial autocorrelation property in the geophysical data. Weights of the linear combination are defined, in order to reflect the inverse distance of the unseen data to each cluster geometry. The cluster geometry is represented through shape-dependent sampling of geographic coordinates of clustered stations. Experiments performed with several data collections investigate the trade-off between the summarization capability and predictive accuracy of the presented interpolative clustering algorithm.

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Loglisci CAppice AMalerba D(2016)Collective regression for handling autocorrelation of network data in a transductive settingJournal of Intelligent Information Systems10.1007/s10844-015-0361-846:3(447-472)Online publication date: 1-Jun-2016
https://dl.acm.org/doi/10.1007/s10844-015-0361-8
Appice AGuccione P(2016)Exploiting Spatial Correlation of Spectral Signature for Training Data Selection in Hyperspectral Image ClassificationDiscovery Science10.1007/978-3-319-46307-0_19(295-309)Online publication date: 19-Oct-2016
https://dl.acm.org/doi/10.1007/978-3-319-46307-0_19
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cover image Data Mining and Knowledge Discovery

Data Mining and Knowledge Discovery Volume 28, Issue 5-6

September 2014

482 pages

ISSN:1384-5810

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Copyright © Copyright © 2014 The Author(s).

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Kluwer Academic Publishers

United States

Publication History

Published: 01 September 2014

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Cited By

Rizzo GMeo RPensa RFalcone GTroncy R(2017)Shaping City Neighborhoods Leveraging Crowd SensorsInformation Systems10.1016/j.is.2016.06.00964:C(368-378)Online publication date: 1-Mar-2017
https://dl.acm.org/doi/10.1016/j.is.2016.06.009
Loglisci CAppice AMalerba D(2016)Collective regression for handling autocorrelation of network data in a transductive settingJournal of Intelligent Information Systems10.1007/s10844-015-0361-846:3(447-472)Online publication date: 1-Jun-2016
https://dl.acm.org/doi/10.1007/s10844-015-0361-8
Appice AGuccione P(2016)Exploiting Spatial Correlation of Spectral Signature for Training Data Selection in Hyperspectral Image ClassificationDiscovery Science10.1007/978-3-319-46307-0_19(295-309)Online publication date: 19-Oct-2016
https://dl.acm.org/doi/10.1007/978-3-319-46307-0_19
Borchani HVarando GBielza CLarrañaga P(2015)A survey on multi-output regressionWiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery10.1002/widm.11575:5(216-233)Online publication date: 1-Sep-2015
https://dl.acm.org/doi/10.1002/widm.1157

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