Do machine learning methods used in data mining enhance the potential of decision support systems? A review for the urban water sector.
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- AI Communications, 2016, v. 29, n. 6, p. 747, doi. 10.3233/AIC-160714
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- Publication type:
- Article
Works matching IS 09217126 AND DT 2016 AND VI 29 AND IP 6
Results: 9
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Use of symbolic dynamic time warping in hierarchical clustering of urban fabric evolutions extracted from spatiotemporal topographic databases.
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- AI Communications, 2016, v. 29, n. 6, p. 733, doi. 10.3233/AIC-160717
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- Article
3
SAX-quantile based multiresolution approach for finding heatwave events in summer temperature time series.
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- AI Communications, 2016, v. 29, n. 6, p. 725, doi. 10.3233/AIC-160716
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- Article
4
Development and selection of decision trees for water management: Impact of data preprocessing, algorithms and settings.
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- AI Communications, 2016, v. 29, n. 6, p. 711, doi. 10.3233/AIC-160711
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- Article
5
Efficient automated quality assessment: Dealing with faulty on-line water quality sensors.
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- AI Communications, 2016, v. 29, n. 6, p. 701, doi. 10.3233/AIC-160713
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- Article
6
A rotation-invariant feature space according to environmental applications needs in a data mining system using fish otoliths.
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- AI Communications, 2016, v. 29, n. 6, p. 687, doi. 10.3233/AIC-160715
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- Article
7
Analysing the effects of water quality on the occurrence of freshwater macroinvertebrate taxa among tropical river basins from different continents.
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- AI Communications, 2016, v. 29, n. 6, p. 665, doi. 10.3233/AIC-160712
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- Publication type:
- Article
8
A survey on pre-processing techniques: Relevant issues in the context of environmental data mining.
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- AI Communications, 2016, v. 29, n. 6, p. 627, doi. 10.3233/AIC-160710
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- Article
9
Special Issue on Environmental Data Mining.
- Published in:
- AI Communications, 2016, v. 29, n. 6, p. 621, doi. 10.3233/AIC-160718
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- Publication type:
- Article