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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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Centroid index: Cluster level similarity measure Presenter : YAN-SHOU SIE Authors : Pasi Fränti,Mohammad Rezaei, Qinpei Zhao2014. PR
Outlines • Motivation • Objectives • Methodology • Experiments • Conclusions • Comments
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.
Objectives • We propose a cluster level measure to estimate the similarity of two clustering solutions.
Methodology • Cluster level similarity • Duality of centroids and partition • Centroid index
Methodology • Point-level differences
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.
Comments • Advantages • Can do Cluster-level measure. • Applications - Similarity measure.