A Distributed Shared Nearest Neighbors Clustering Algorithm

  • Juan Zamora
  • Héctor Allende-Cid
  • Marcelo Mendoza
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10657)


Current data processing tasks require efficient approaches capable of dealing with large databases. A promising strategy consists in distributing the data along several computers that partially solves the undertaken problem. Then, these partial answers are integrated in order to obtain a final solution. We introduce the Distributed Shared Nearest Neighbor based clustering algorithm (D-SNN) which is able to work with disjoint partitions of data producing a global clustering solution that achieves a competitive performance regarding centralized approaches. Our algorithm is suited for large scale problems (e.g, text clustering) where data cannot be handled by a single machine due to memory size constraints. Experimental results over five data sets show that our proposal is competitive in terms of standard clustering quality performance measures.


Clustering Distributed algorithm Shared Nearest Neighbors 



Juan Zamora is supported by a postdoctoral project from Pontificia Universidad Católica de Valparaíso. Héctor Allende-Cid is supported by project FONDECYT initiation into research 11150248. Marcelo Mendoza was supported by project Basal FB0821.


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Juan Zamora
    • 1
  • Héctor Allende-Cid
    • 1
  • Marcelo Mendoza
    • 2
    • 3
  1. 1.Pontificia Universidad Católica de ValparaísoValparaísoChile
  2. 2.Universidad Técnica Federico Santa MaríaSantiagoChile
  3. 3.Centro Científico y Tecnológico de ValparaísoValparaísoChile

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