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Presenter : YAN-SHOU SIE Authors : Pasi Fränti, Mohammad Rezaei, Qinpei Zhao 2014 . PR

Centroid index: Cluster level similarity measure. Presenter : YAN-SHOU SIE Authors : Pasi Fränti, Mohammad Rezaei, Qinpei Zhao 2014 . PR. Outlines. Motivation Objectives Methodology Experiments Conclusions Comments. Motivation.

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Presenter : YAN-SHOU SIE Authors : Pasi Fränti, Mohammad Rezaei, Qinpei Zhao 2014 . PR

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  1. Centroid index: Cluster level similarity measure Presenter : YAN-SHOU SIE Authors : Pasi Fränti,Mohammad Rezaei, Qinpei Zhao2014. PR

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

  3. Motivation • Despite this, all external cluster validity indexes calculate only point-level differences of two partitions without any direct information about how similar their cluster-level structures are.

  4. Objectives • We propose a cluster level measure to estimate the similarity of two clustering solutions.

  5. Methodology • Cluster level similarity • Duality of centroids and partition • Centroid index

  6. Methodology

  7. Methodology

  8. Methodology • Point-level differences

  9. Experiments

  10. Experiments

  11. Experiments

  12. Experiments

  13. Experiments

  14. Experiments

  15. Experiments

  16. Experiments

  17. Experiments

  18. Conclusions • We have introduced a cluster level similarity measure called centroid index (CI), which has clear intuitive interpretation by corresponding to the number of differently allocated clusters.

  19. Comments • Advantages • Can do Cluster-level measure. • Applications - Similarity measure.

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