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Stream quantiles via maximal entropy histograms

Arandjelović, Ognjen, Pham, Ducson and Venkatesh, Svetha 2014, Stream quantiles via maximal entropy histogramsNeural information processing : 21st International Conference ICONIP 2014 Kuching, Malaysia, November 3-6, 2014 Proceedings, Part II, Springer Verlag, Berlin, Gemany, pp.327-334.

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Title Stream quantiles via maximal entropy histograms
Author(s) Arandjelović, Ognjen
Pham, Ducson
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Title of book Neural information processing : 21st International Conference ICONIP 2014 Kuching, Malaysia, November 3-6, 2014 Proceedings, Part II
Publication date 2014
Series Lecture Notes in Computer Science ; v.8835
Chapter number 40
Total chapters 71
Start page 327
End page 334
Total pages 8
Publisher Springer Verlag
Place of Publication Berlin, Gemany
Summary We address the problem of estimating the running quantile of a data stream when the memory for storing observations is limited.We (i) highlight the limitations of approaches previously described in the literature which make them unsuitable for non-stationary streams, (ii) describe a novel principle for the utilization of the available storage space, and (iii) introduce two novel algorithms which exploit the proposed principle. Experiments on three large realworld data sets demonstrate that the proposed methods vastly outperform the existing alternatives.
ISBN 9783319126395
ISSN 0302-9743
1611-3349
Language eng
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category B1 Book chapter
ERA Research output type B Book chapter
Copyright notice ©2014, Springer Verlag
Persistent URL http://hdl.handle.net/10536/DRO/DU:30070816

Document type: Book Chapter
Collection: Centre for Pattern Recognition and Data Analytics
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