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Mean-shift outlier detection

Mean-shift outlier detection. Jiawei Yang Susanto Rahardja Pasi Fränti. 18.11.2018. Clustering. Clustering with noisy data. How to deal with outliers in clustering. Approch 1:. Cluster. Approch 2:. Outlier removal. Cluster. Approch 3:. Outlier removal. Cluster.

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Mean-shift outlier detection

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  1. Mean-shift outlier detection Jiawei Yang Susanto Rahardja Pasi Fränti 18.11.2018

  2. Clustering

  3. Clustering with noisy data

  4. How to deal with outliers in clustering Approch 1: Cluster Approch 2: Outlier removal Cluster Approch 3: Outlier removal Cluster Mean-shift approch: Meanshift Cluster

  5. Mean-shift

  6. K-nearest neighbors k=4 K-neighborhood Point

  7. Expected result Mean of neighbors Point

  8. Result with noise point Mean of neighbors Noise point K-neighborhood

  9. Part I: Noise removal Fränti and Yang, "Medoid-shift noise removal to improve clustering", Int. Conf. Artificial Intelligence and Soft Computing (CAISC), June 2018.

  10. Mean-shift process Move points to the means of their neighbors

  11. Mean or Medoid? Mean-shift Medoid-shift

  12. Medoid-shift algorithm Fränti and Yang, "Medoid-shift noise removal to improve clustering", Int. Conf. Artificial Intelligence and Soft Computing (CAISC), June 2018. • REPEAT 3 TIMES • Calculate kNN(x) • Calculate medoid M of the neighbors • Replace point x by the medoid M

  13. Iterative processes

  14. Effect on clustering result Noisy original Iteration 1 CI=4 CI=4 Iteration 2 Iteration 3 CI=3 CI=0

  15. Experiments

  16. Datasets P. Fränti and S. Sieranoja, "K-means properties on six clustering benchmark datasets", Applied Intelligence, 2018. k=35 k=20 k=50 N=5000 N=5000 k=15 k=15 N=5000 N=5000 k=15 k=15

  17. Noise types Random noise Random noise S1 S3 S3 S1 Data-dependent noise Data-dependent noise

  18. Results for noise type 1 Centroid Index (CI) CI reduces from 5.1 to 1.4 KM: K-means with random initialization RS: P. Fränti, "Efficiency of random swap clustering", Journal of Big Data, 5:13, 1-29, 2018.

  19. Results for noise type 2 Centroid Index (CI) CI reduces from 5.0 to 1.1 KM: K-means with random initialization RS: P. Fränti, "Efficiency of random swap clustering", Journal of Big Data, 5:13, 1-29, 2018.

  20. Part II: Outlier detection Yang, Rahardja, Fränti, "Mean-shift outlier detection", Int. Conf. Fuzzy Systems and Data Mining (FSDM), November 2018.

  21. Outlier scores D=|x-y| 203 12 k=4

  22. Mean-shift for Outlier Detection • Algorithm: MOD • Calculate Mean-shift y for every point x • Outlier score Di = |yi - xi| • SD = Standard deviation of all Di • IF Di > SD THEN xi is OUTLIER

  23. Result after three iterations x D=|x-y3| y3 y1 y2

  24. Outlier scores D=|x-y| 69 203 65 36 13 64 26 44 28 14 40 30 120 187 12 4 46 27 16 58 118 83 95 SD=52

  25. Distribution of the scores Outliers Normal points

  26. Experimental comparisons

  27. Results for Noise type 1 A priori noise level 7% Automatically estimated (SD)

  28. Results for Noise type 2 A priori noise level 7% Automatically estimated (SD)

  29. Conclusions • Why to use: • Simple but effective! • How it performs: • Outperforms existing methods! • Usefulness: • No threshold parameter needed! 98-99%

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