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Presenter : JHOU, YU-LIANG Authors : Yiu-ming Cheung, Hong Jia 2013,PR

Categorical-and-numerical-attribute data clustering based on a unified similarity metric without knowing cluster number. Presenter : JHOU, YU-LIANG Authors : Yiu-ming Cheung, Hong Jia 2013,PR. Outlines. Motivation Objectives Methodology Experiments Conclusions Comments.

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Presenter : JHOU, YU-LIANG Authors : Yiu-ming Cheung, Hong Jia 2013,PR

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  1. Categorical-and-numerical-attribute data clustering based on a unified similarity metric without knowing cluster number Presenter : JHOU, YU-LIANGAuthors : Yiu-mingCheung, Hong Jia2013,PR

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

  3. Motivation • It is a nontrivial task to perform clustering on mixed data because there exists an awkward gap between the similarity metrics for categorical and numerical data.

  4. Objectives • This paper presents a general clustering framework based on the concept of object-cluster similarity and gives a unified similarity metric which can be applied to the data with categorical, numerical, and mixed attributes.

  5. Methodologyobject-cluster similarity metric categorical attribute

  6. Methodologyobject-cluster similarity metric • numerical attributes • mixed data

  7. MethodologyIterative clustering algorithm

  8. MethodologyAutomatic selection of cluster number Competition mechanism

  9. MethodologyAutomatic selection of cluster number Penalized mechanism

  10. Experiments-data sets

  11. Experiments mixed data

  12. Experiments categorical data

  13. Conclusions • We adopt our new approach can improve the time-consumingand efficiency of the process and overcome the cluster number selection problem.

  14. Comments • Advantages More save time and efficiency. Applications -Clustering

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