Soft Computing

, Volume 22, Issue 24, pp 8243–8258 | Cite as

Enhanced shared nearest neighbor clustering approach using fuzzy for teleconnection analysis

  • Rika SharmaEmail author
  • Kesari Verma
Methodologies and Application


Massive amount of Earth science data open an unprecedented opportunity to discover potentially valuable information. Earth science data are complex, nonlinear, high-dimensional data, and the sparsity of data in high-dimensional space poses major challenge in clustering of the data. Shared nearest neighbor clustering (SNN) algorithm is one of the well-known and efficient methods to handle high-dimensional spatiotemporal data. The SNN clustering method does not cluster all the data forming rigid boundary selection. This paper reports fuzzy shared nearest neighbor (FSNN) algorithm which is an enhancement of the SNN clustering method that has the capability of handling the data lying in the boundary regions by means of a fuzzy concept. The clusters obtained can be characterized by the cluster centroid, which summarizes the behavior of the ocean points in the cluster. The statistical measure is used to find the significant relation between the cluster centroids and the existing climate indices. In this study, correlation measure is used to find the significant pattern, such as teleconnection or dipole. The experimentation is performed on Indian continent latitude range \(7.5^{\circ }{-}37.5^{\circ }\hbox {N}\) and longitude range \(67.5^{\circ }{-}97.5^{\circ }\hbox {E}\). Extensive experiments are carried out to compare the proposed approach with existing clustering methods such as K-means, fuzzy C-means and SNN. The proposed method, FSNN algorithm, not only handles the data lying in the overlapping region, but it also finds more compact and well-separated clusters. FSNN shows better results in terms of finding a significant correlation between cluster centroids and existing climate indices and validated by ground truth dataset.


Clustering Shared nearest neighbor Fuzzy shared nearest neighbor Earth science data Climate indices 


Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


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Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.Department of Computer ApplicationsNational Institute of Technology RaipurRaipurIndia

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