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Presenter : Min-Cong Wu Authors : Chantal Hajjar , Hani Hamdan 2013.NN

Interval data clustering using self-organizing maps based on adaptive Mahalanobis distances. Presenter : Min-Cong Wu Authors : Chantal Hajjar , Hani Hamdan 2013.NN. Outlines. Motivation Objectives Methodology Experiments Conclusions Comments. Motivation.

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Presenter : Min-Cong Wu Authors : Chantal Hajjar , Hani Hamdan 2013.NN

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  1. Interval data clustering using self-organizing maps based on adaptive Mahalanobisdistances Presenter : Min-Cong WuAuthors : Chantal Hajjar, Hani Hamdan2013.NN

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

  3. Motivation • In real world applications, data may not be formatted as single values, may are represented by interval. • but about self-organizing map for interval-valued data based on adaptive that's method haven't be proposed a lot.

  4. Objectives • we proposed two methods, Both methods use the Mahalanobis distance to find the best matching unit of an interval data vector.

  5. Methodology -Mahalanobis distance Input: Interval data Ex.temperatures

  6. Methodology -Mahalanobis distance process: R1={[1,2],[3,4],[5,6],[7,8]} RiL=(2,4,6,8). RiU=(1,3,5,7). find Ri’s BMU

  7. Methodology -Mahalanobis distance

  8. Methodology -Computing the prototype vectors Until t=total neighborhood radius Neuron c, Neuron k

  9. Methodology-intSOM_MCDC(m1) totallter↑,σ(t) ↓, because σ init>σfinal

  10. Methodology -intSOM_MDDC(m2)application and training first phase = use common distance 90% iterations second phase = use different distance 10% iterations

  11. Methodology - SOM quality evaluation the topographic error (tpe) measures the degree of topology preservation data classification error (dce) percentage of misclassified data vectors

  12. Experiment – Artificial interval data set

  13. Experiment - Clustering results

  14. Experiment - Clustering results

  15. Experiment - Real temperature interval data set tpe=4.7 tpe=6.6 tpe=6.6

  16. Experiment - Clustering results and interpretation 17.36 taking the monthly average temperatures

  17. Experiment - Comparison with other methods-Simulated data

  18. Experiment - Comparison with other methods-French meteorological real data set 23, 28 and 42 mounted in northeastern regions 24 and 23 mounted in western regions 12.71<13.89

  19. Conclusions • we proposed two methods, the second method is more adaptive than the first one because it uses a different distance per cluster in the last iterations of the training algorithm.

  20. Comments • Advantages - a better topology preservation. Applications - self organizing map

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