Applying Anomalous Cluster Approach to Spatial Clustering

Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 683)

Abstract

The concept of anomalous clustering applies to finding individual clusters on a digital geography map supplied with a single feature such as brightness or temperature. An algorithm derived within the individual anomalous cluster framework extends the so-called region growing algorithms. Yet our approach differs in that the algorithm parameter values are not expert-driven but rather derived from the anomalous clustering model. This novel framework successfully applies to the issue of automatically delineating coastal upwelling from Sea Surface Temperature (SST) maps, a natural phenomenon seasonally occurring in coastal waters.

Notes

Acknowledgements

The authors thank the colleagues of Centro de Oceanografia and Department de Engenharia Geográfica, Geofísica e Energia (DEGGE), Faculdade de Ciências, Universidade de Lisboa for providing the SST images examined in this study. The authors are thankful to the anonymous reviewers for their insightful and constructive comments that allowed us to improve our paper.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Computer Science and NOVA Laboratory for Computer Science and Informatics (NOVA LINCS)Faculdade de Ciências e Tecnologia, Universidade Nova de LisboaCaparicaPortugal
  2. 2.Department of Data Analysis and Machine IntelligenceNational Research University Higher School of EconomicsMoscowRussian Federation
  3. 3.Department of Computer ScienceBirkbeck University of LondonLondonUK

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