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14 Nov 2017
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20 Sep 2017
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13 May 2016
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12 Oct 2015
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27 May 2015
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23 Mar 2015
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19 Mar 2015
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17 Feb 2014
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30 Sep 2013
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12 Mar 2013
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20 Feb 2013
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18 Nov 2012
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18 Nov 2012
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25 May 2010
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11 Jun 2009
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20 May 2009
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20 Jan 2009
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16 Jun 2008
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19 May 2008
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06 Feb 2008
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14 Jun 2019
Applied Statistics 2019
07 Feb 2019
ESRA 2019 Zagreb, 15. - 19. 7 . 2019
22 Jan 2013
Information Visualization MOOC
18 May 2011
CASS - Courses in Applied Social Surveys
Vabilo k oddaji prispevka za delavnico Symbolic Data Analysis, do 22.7. |
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Vljudno vabljeni na delavnico na temo Symbolic Data Analysis - SDA2018, bo potekala med 18 in 20 oktobrom 2018 na Politehničnem institutu v Viana do Castelo. Rok za oddajo prispevkov je 22 julij. Več o delavnici na naslovu https://sda2018.wixsite.com/ipvc.
Symbolic Data Analysis (SDA) provides a framework for the representation and analysis of data that comprehends inherent variability. While in Data Mining and classical Statistics the data to be analyzed usually presents one single value for each variable, that is no longer the case when the entities under analysis are not single elements, but groups gathered on the basis of some given criteria. Then, for each variable, variability inherent to each group should be taken into account. Also, when analysing concepts, such as botanic species, disease descriptions, car models, and so on, data entail intrinsic variability, which should be explicitly considered. To this purpose, new variable types have been introduced, whose realizations are not single real values or categories, but sets, intervals, or,more generally, distributions over a given domain. SDA provides methods for the (multivariate) analysis of such data, where the variability expressed in the data representation is taken into account, using various approaches. Brito, P. (2014). Symbolic data analysis: Another look at the interaction of data mining and statistics. WIREs Data Mining and Knowledge Discovery, 4 (4), 281–295. doi: 10.1002/widm.1133
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