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Efficient Algorithms for Pattern Mining in Spatiotemporal Data

by Nagasaranya N
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 106 - Number 8
Year of Publication: 2014
Authors: Nagasaranya N
10.5120/18543-9772

Nagasaranya N . Efficient Algorithms for Pattern Mining in Spatiotemporal Data. International Journal of Computer Applications. 106, 8 ( November 2014), 35-39. DOI=10.5120/18543-9772

@article{ 10.5120/18543-9772,
author = { Nagasaranya N },
title = { Efficient Algorithms for Pattern Mining in Spatiotemporal Data },
journal = { International Journal of Computer Applications },
issue_date = { November 2014 },
volume = { 106 },
number = { 8 },
month = { November },
year = { 2014 },
issn = { 0975-8887 },
pages = { 35-39 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume106/number8/18543-9772/ },
doi = { 10.5120/18543-9772 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:38:53.673766+05:30
%A Nagasaranya N
%T Efficient Algorithms for Pattern Mining in Spatiotemporal Data
%J International Journal of Computer Applications
%@ 0975-8887
%V 106
%N 8
%P 35-39
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Spatio-temporal data is any information relating to space and time. It is continually updated data with 1TB/hr are greatly challenging our ability to digest the data. With that data, it is unable to gain exact information. Data mining models contains many statistical models such as regression models of various kinds, cluster analysis models, covariance analysis models, principle component analysis models, outlier detection models(temporal, spatial, non-spatial), trend detection models, partial least squares models(prediction) and multiple variant visualization models. Most of these models find applications in spatial data mining and pattern discovery.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Spatio-Temporal Data Clustering Association Rule Pattern Discovery

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