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Sixth IEEE International Conference on Data Mining (ICDM'06)
The PDD Framework for Detecting Categories of Peculiar Data
Hong Kong
December 18-December 22
ISBN: 0-7695-2701-9
ASCII Text
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Mahesh Shrestha, Howard J. Hamilton, Yiyu Yao, Ken Konkel, Liqiang Geng, "The PDD Framework for Detecting Categories of Peculiar Data," Data Mining, IEEE International Conference on, pp. 562-571, Sixth IEEE International Conference on Data Mining (ICDM'06), 2006.
 
 
BibTex
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@article{ 10.1109/ICDM.2006.159,
author = {Mahesh Shrestha and Howard J. Hamilton and Yiyu Yao and Ken Konkel and Liqiang Geng},
title = {The PDD Framework for Detecting Categories of Peculiar Data},
journal ={Data Mining, IEEE International Conference on},
volume = {0},
year = {2006},
issn = {1550-4786},
pages = {562-571},
doi = {http://doi.ieeecomputersociety.org/10.1109/ICDM.2006.159},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Data Mining, IEEE International Conference on
TI - The PDD Framework for Detecting Categories of Peculiar Data
SN - 1550-4786
SP562
EP571
A1 - Mahesh Shrestha,
A1 - Howard J. Hamilton,
A1 - Yiyu Yao,
A1 - Ken Konkel,
A1 - Liqiang Geng,
PY - 2006
KW - null
VL - 0
JA - Data Mining, IEEE International Conference on
ER -
 
Mahesh Shrestha, University of Regina, Canada
Howard J. Hamilton, University of Regina, Canada
Yiyu Yao, University of Regina, Canada
Ken Konkel, University of Regina, Canada
Liqiang Geng, University of Regina, Canada
Peculiar data are objects that are relatively few in number and significantly different from the other objects in a data set. In this paper, we propose the PDD framework for detecting multiple categories of peculiar data. This framework provides an extensible set of perspectives for viewing data, currently including viewing data as a set of records, attributes, frequencies, intervals, sequences, or sequences of changes. By using these six views of the data, multiple categories of peculiar data can be detected to reveal different aspects of the data. For each view, the framework provides an extensible set of peculiarity measures to detect outliers and other kinds of peculiar data. The PDD framework has been implemented for Oracle and Access. Experiments are reported for data sets concerning Regina weather and NHL hockey.
Citation:
Mahesh Shrestha, Howard J. Hamilton, Yiyu Yao, Ken Konkel, Liqiang Geng, "The PDD Framework for Detecting Categories of Peculiar Data," icdm, pp.562-571, Sixth IEEE International Conference on Data Mining (ICDM'06), 2006
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