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Presenter : Chuang, Kai-Ting Authors : Guillaume Cleuziou* 2013, PRL

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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Presenter : Chuang, Kai-Ting Authors : Guillaume Cleuziou* 2013, PRL

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  1. OSOM: A method for building overlapping topological maps Presenter : Chuang, Kai-TingAuthors : Guillaume Cleuziou*2013, PRL

  2. Outlines • Motivation • Objectives • Methodology • Experiments • Conclusions • Comments

  3. 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.

  4. 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.

  5. Methodology-Framework

  6. Methodology OSOM SOM

  7. Methodolog-fast-osom

  8. Experiment-dataset

  9. Experiment-evaluation framework

  10. Experiment results

  11. Experiment-Topological evaluation

  12. Conclusions • Ensure the algorithm to converge and then bring solutions to the motivationsmentioned: limited complexity, topological correctness, etc.

  13. Comments • Advantages • The OSOM is simple method. • Applications • Topological maps.

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