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A framework for clustering uncertain data

A framework for clustering uncertain data <jats:p>The challenges associated with handling uncertain data, in particular with querying and mining, are finding increasing attention in the research community. Here we focus on clustering uncertain data and describe a general framework for this purpose that also allows to visualize and understand the impact of uncertainty---using different uncertainty models---on the data mining results. Our framework constitutes release 0.7 of ELKI (http://elki.dbs.ifi.lmu.de/) and thus comes along with a plethora of implementations of algorithms, distance measures, indexing techniques, evaluation measures and visualization components.</jats:p> http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Proceedings of the VLDB Endowment CrossRef

A framework for clustering uncertain data

Proceedings of the VLDB Endowment , Volume 8 (12): 1976-1979 – Aug 1, 2015

A framework for clustering uncertain data


Abstract

<jats:p>The challenges associated with handling uncertain data, in particular with querying and mining, are finding increasing attention in the research community. Here we focus on clustering uncertain data and describe a general framework for this purpose that also allows to visualize and understand the impact of uncertainty---using different uncertainty models---on the data mining results. Our framework constitutes release 0.7 of ELKI (http://elki.dbs.ifi.lmu.de/) and thus comes along with a plethora of implementations of algorithms, distance measures, indexing techniques, evaluation measures and visualization components.</jats:p>

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Publisher
CrossRef
ISSN
2150-8097
DOI
10.14778/2824032.2824115
Publisher site
See Article on Publisher Site

Abstract

<jats:p>The challenges associated with handling uncertain data, in particular with querying and mining, are finding increasing attention in the research community. Here we focus on clustering uncertain data and describe a general framework for this purpose that also allows to visualize and understand the impact of uncertainty---using different uncertainty models---on the data mining results. Our framework constitutes release 0.7 of ELKI (http://elki.dbs.ifi.lmu.de/) and thus comes along with a plethora of implementations of algorithms, distance measures, indexing techniques, evaluation measures and visualization components.</jats:p>

Journal

Proceedings of the VLDB EndowmentCrossRef

Published: Aug 1, 2015

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