Dealing with spatial autocorrelation when learning predictive clustering trees

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Dealing with spatial autocorrelation when learning predictive clustering trees

a Jožef Stefan Institute, Department of Knowledge Technologies, Jamova cesta 39, 1000 Ljubljana, Slovenia b Jožef Stefan International Postgraduate School, Jamova 39, 1000 Ljubljana, Slovenia c Dipartimento di Informatica, Università degli Studi di Bari “Aldo Moro”, via Orabona 4, 70125 Bari, Italy d Centre of Excellence for Integrated Approaches in Chemistry and Biology of Proteins, Jamova 39,...

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Global and Local Spatial Autocorrelation in Predictive Clustering Trees

Spatial autocorrelation is the correlation among data values, strictly due to the relative location proximity of the objects that the data refer to. This statistical property clearly indicates a violation of the assumption of observation independence a pre-condition assumed by most of the data mining and statistical models. Inappropriate treatment of data with spatial dependencies could obfusca...

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Relational Ranking with Predictive Clustering Trees

A novel class of applications of predictive clustering trees is addressed, namely relational ranking. Predictive clustering trees, as implemented in TILDE, allow for predicting multiple target variables from relational data. This approach makes sense especially if the target variables are not independent of each other. This is typically the case in ranking, where the (relative) performance of s...

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(Inductive) Querying Environment for Predictive Clustering Trees

Inductive databases tightly integrate databases with data mining. Besides data, an inductive database also stores models that have been obtained by running data mining algorithms on the data. By means of a querying environment, the user can query the database and retrieve particular models. In this paper, we propose such a querying environment. It can be used for building new models and for sea...

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ژورنال

عنوان ژورنال: Ecological Informatics

سال: 2013

ISSN: 1574-9541

DOI: 10.1016/j.ecoinf.2012.10.006