Applying Anomalous Cluster Approach to Spatial Clustering

Research output: Chapter in Book/Report/Conference proceedingChapter

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.
Original languageEnglish
Title of host publicationUncertainty Modeling
Subtitle of host publicationDedicated to Professor Boris Kovalerchuk on his Anniversary
EditorsVladik Kreinovich
PublisherSpringer International Publishing Switzerland
Pages147-157
Volume683
Edition1
ISBN (Electronic)978-3-319-51052-1
ISBN (Print)978-3-319-51051-4
DOIs
Publication statusPublished - 1 Feb 2017

Publication series

NameStudies in Computational Intelligence
Volume683

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Nascimento, S., & Mirkin, B. (2017). Applying Anomalous Cluster Approach to Spatial Clustering. In V. Kreinovich (Ed.), Uncertainty Modeling: Dedicated to Professor Boris Kovalerchuk on his Anniversary (1 ed., Vol. 683, pp. 147-157). (Studies in Computational Intelligence; Vol. 683). Springer International Publishing Switzerland. https://doi.org/10.1007/978-3-319-51052-1_10