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Making Sense of Sensor Data
April-June 2007 (vol. 6 no. 2)
pp. 105-108
ASCII Text
x 
 
Diane J. Cook, "Making Sense of Sensor Data," IEEE Pervasive Computing, vol. 6, no. 2, pp. 105-108, April-June, 2007.
 
 
BibTex
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@article{ 10.1109/MPRV.2007.37,
author = {Diane J. Cook},
title = {Making Sense of Sensor Data},
journal ={IEEE Pervasive Computing},
volume = {6},
number = {2},
issn = {1536-1268},
year = {2007},
pages = {105-108},
doi = {http://doi.ieeecomputersociety.org/10.1109/MPRV.2007.37},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - MGZN
JO - IEEE Pervasive Computing
TI - Making Sense of Sensor Data
IS - 2
SN - 1536-1268
SP105
EP108
EPD - 105-108
A1 - Diane J. Cook,
PY - 2007
KW - sensors
KW - sensor data
KW - data analysis
KW - Principle Components Analysis
KW - Singular Value Decomposition
KW - clustering algorithms
KW - association analysis
KW - Episode Discovery
VL - 6
JA - IEEE Pervasive Computing
ER -
 
As sensors become more ubiquitous, our need to analyze sensor data grows. This is a complex task; sensor data has unique features that challenge conventional data-analysis techniques. This column examines tools and algorithms for analyzing sensor data.
Index Terms:
sensors, sensor data, data analysis, Principle Components Analysis, Singular Value Decomposition, clustering algorithms, association analysis, Episode Discovery
Citation:
Diane J. Cook, "Making Sense of Sensor Data," IEEE Pervasive Computing, vol. 6, no. 2, pp. 105-108, Apr.-June 2007, doi:10.1109/MPRV.2007.37
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