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OSOM: A method for building overlapping topological maps. Presenter : Chuang, Kai-Ting Authors : Guillaume Cleuziou* 2013, PRL. Outlines. Motivation Objectives Methodology Experiments Conclusions Comments. Motivation.

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Presentation Transcript
outlines
Outlines
  • Motivation
  • Objectives
  • Methodology
  • Experiments
  • Conclusions
  • Comments
motivation
Motivation
  • Overlapping clustering solutions extract data organizations that are more fitted to the input data than crisp clustering solutions.
  • Unsupervised neural networks bring efficient solutions to visualize class structures.
objectives
Objectives
  • We present the algorithm O-SOM that uses both an overlapping variant of the k-means clustering algorithm and the well known Kohonenapproach,in order to build overlapping topologic maps.
  • To solve problems that are recurrent in overlapping clustering: number of clusters, complexity of the algorithm and coherence of the overlaps.
conclusions
Conclusions
  • Ensure the algorithm to converge and then bring solutions to the motivationsmentioned: limited complexity, topological correctness, etc.
comments
Comments
  • Advantages
    • The OSOM is simple method.
  • Applications
    • Topological maps.