Spreading Activation Model for Connectivity Based Clustering

  • QingYuan Huang
  • JinShu Su
  • YingZhi Zeng
  • YongJun Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4243)


Connectivity based clustering has wide application in many networks like ad hoc networks, sensor networks and so on. But traditional research on this aspect is mainly based on graph theory, which needs global knowledge of the whole network. In this paper, we propose a intelligent approach called spreading activation models for connectivity based clustering (SAMCC) scheme that only local information is needed for clustering. The main feature of SAMCC scheme is applying the idea of spreading activation, which is an organization method for human long-term memory, to clustering and the whole network can be clustered in a decentralized automatic and parallel manner. The SAMCC scheme can be scaled to different networks and different level clustering. Experiment evaluations show the efficiency of our SAMCC scheme in clustering accuracy.


Neighboring Node Semantic Network Network Graph Cluster Scheme Spreading Activation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • QingYuan Huang
    • 1
  • JinShu Su
    • 1
  • YingZhi Zeng
    • 1
  • YongJun Wang
    • 1
  1. 1.School of ComputerNational University of Defense TechnologyChangShaChina

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