caa a22 4500 445331771 CHVBK 20180317142739.0 cr unu---uuuuu 170323e20110101xx s 000 0 eng 10.1007/s10707-010-0111-6 doi (NATIONALLICENCE)springer-10.1007/s10707-010-0111-6 A framework for regional association rule mining and scoping in spatial datasets [Elektronische Daten] [Wei Ding, Christoph Eick, Xiaojing Yuan, Jing Wang, Jean-Philippe Nicot] The motivation for regional association rule mining and scoping is driven by the facts that global statistics seldom provide useful insight and that most relationships in spatial datasets are geographically regional, rather than global. Furthermore, when using traditional association rule mining, regional patterns frequently fail to be discovered due to insufficient global confidence and/or support. In this paper, we systematically study this problem and address the unique challenges of regional association mining and scoping: (1) region discovery: how to identify interesting regions from which novel and useful regional association rules can be extracted; (2) regional association rule scoping: how to determine the scope of regional association rules. We investigate the duality between regional association rules and regions where the associations are valid: interesting regions are identified to seek novel regional patterns, and a regional pattern has a scope of a set of regions in which the pattern is valid. In particular, we present a reward-based region discovery framework that employs a divisive grid-based supervised clustering for region discovery. We evaluate our approach in a real-world case study to identify spatial risk patterns from arsenic in the Texas water supply. Our experimental results confirm and validate research results in the study of arsenic contamination, and our work leads to the discovery of novel findings to be further explored by domain scientists. Springer Science+Business Media, LLC, 2010 Association rule mining and scoping nationallicence Region discovery nationallicence Clustering nationallicence Spatial data mining nationallicence Ding Wei Department of Computer Science, University of Massachusetts-Boston, 02125-3393, Boston, MA, USA aut Eick Christoph Department of Computer Science, University of Houston, 77004, Houston, TX, USA aut Yuan Xiaojing Engineering Technology Department, University of Houston, 77004, Houston, TX, USA aut Wang Jing Department of Computer Science, University of Houston, 77004, Houston, TX, USA aut Nicot Jean-Philippe Bureau of Economic Geology, John A. & Katherine G. Jackson School of Geosciences, The University of Texas at Austin, Austin, TX, USA aut GeoInformatica Springer US; http://www.springer-ny.com 15/1(2011-01-01), 1-28 1384-6175 15:1<1 2011 15 10707 https://doi.org/10.1007/s10707-010-0111-6 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s10707-010-0111-6 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Ding Wei Department of Computer Science, University of Massachusetts-Boston, 02125-3393, Boston, MA, USA aut NATIONALLICENCE 700 1- Eick Christoph Department of Computer Science, University of Houston, 77004, Houston, TX, USA aut NATIONALLICENCE 700 1- Yuan Xiaojing Engineering Technology Department, University of Houston, 77004, Houston, TX, USA aut NATIONALLICENCE 700 1- Wang Jing Department of Computer Science, University of Houston, 77004, Houston, TX, USA aut NATIONALLICENCE 700 1- Nicot Jean-Philippe Bureau of Economic Geology, John A. & Katherine G. Jackson School of Geosciences, The University of Texas at Austin, Austin, TX, USA aut NATIONALLICENCE 773 0- GeoInformatica Springer US; http://www.springer-ny.com 15/1(2011-01-01), 1-28 1384-6175 15:1<1 2011 15 10707 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer