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A Modified Approach to Inferring Animal Social Networks from Spatiotemporal Data Streams

  • Pu Zhang
  • Qiang ShenEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 650)

Abstract

Animal social networks offer an important research mechanism for animal behaviour analysis. Inferring social network structures in ecological systems from spatiotemporal data streams [1] presents a procedure to build such networks based on animal’s foraging process data which consists of time and location records. The method clusters the individuals into different gathering events and links up the individuals that appear in the same events, and subsequently filters coincident links. However, the original model does not perform well in many aspects, including time and space complexity and not-unique coincident link filtering threshold. To modify this method, fuzzy c-means is employed in this work to cluster all links into two groups, strong links or weak links. The work presented here also experimentally compares the performance of the proposed modification against the original method, demonstrating the efficacy of the modified version.

Keywords

Animal social networks Coincident links Spatiotemporal data Fuzzy c-means 

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Department of Computer Science, Institute of Mathematics, Physics and Computer ScienceAberystwyth UniversityAberystwythUK

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