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.
|Title of host publication||Uncertainty Modeling|
|Subtitle of host publication||Dedicated to Professor Boris Kovalerchuk on his Anniversary|
|Publisher||Springer International Publishing Switzerland|
|Publication status||Published - 1 Feb 2017|
|Name||Studies in Computational Intelligence|
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