Imperial College London
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ProfessorNiallAdams

Faculty of Natural Sciences, Department of Mathematics

Professor of Statistics
 
 
 
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Contact

 

+44 (0)20 7594 8837n.adams Website

 
 
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Location

 

6M55Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Bakoben:2016:10.1016/j.patrec.2016.03.004,
author = {Bakoben, M and Bellotti, AG and Adams, NM},
doi = {10.1016/j.patrec.2016.03.004},
journal = {Pattern Recognition Letters},
pages = {28--34},
title = {Improving clustering performance by incorporating uncertainty},
url = {http://dx.doi.org/10.1016/j.patrec.2016.03.004},
volume = {77},
year = {2016}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - In more challenging problems the input to a clustering problem is not raw data objects, but rather parametric statistical summaries of the data objects. For example, time series of different lengths may be clustered on the basis of estimated parameters from autoregression models. Such summary procedures usually provide estimates of uncertainty for parameters, and ignoring this source of uncertainty affects the recovery of the true clusters. This paper is concerned with the incorporation of this source of uncertainty in the clustering procedure. A new dissimilarity measure is developed based on geometric overlap of confidence ellipsoids implied by the uncertainty estimates. In extensive simulation studies and a synthetic time series benchmark dataset, this new measure is shown to yield improved performance over standard approaches.
AU  - Bakoben,M
AU  - Bellotti,AG
AU  - Adams,NM
DO  - 10.1016/j.patrec.2016.03.004
EP  - 34
PY  - 2016///
SN  - 1872-7344
SP  - 28
TI  - Improving clustering performance by incorporating uncertainty
T2  - Pattern Recognition Letters
UR  - http://dx.doi.org/10.1016/j.patrec.2016.03.004
UR  - http://hdl.handle.net/10044/1/30380
VL  - 77
ER  -
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