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What’s in a Label?
Business value of “soft” vs “hard” cluster ensembles
                                              solutions-2
                              Nicole Huyghe & Anita Prinzie
Answers the who and the why
Theme 1


Theme 2


Theme 3

          ...

Theme 9
Theme 10


           Cluster
          Ensemble
HARD OR SOFT
CLUSTER ENSEMBLE
Stability   Integrity   Accuracy   Size
Stability




Similarity Index (Lange et al, 2004) indicates the percentage of pairs of observations that belong to the same
cluster in both clustering C and clustering C’.
Cluster Integrity – Heterogeneity




Total separation of clusters: based on the distance between cluster centers
Cluster Integrity - Homogeneity




Scatter (compactness): average ratio of the cluster variance to the variance of the dataset.
Accuracy
                Reality                                                    Prediction

                        5                                                             5
                                                                                              6
                   4        6                                                  4
                                                                                          2

    1         2                                                       1
         3                      7                                                             7
                                                                           3
                       8                                                             8

                            9                                                             9




Adjusted Rand Index (Hubert and Arabie, 1985): level of agreement between the predicted segment and the real
segment correcting for the expected level of agreement.
Size




Uniformity deviation: average deviation from each segment from uniform segment size (1/number of segments).
Rheumatism


Software journey


Osteoporosis
Stability          Heterogeneity


 H>S                     H>S




       Accuracy           Homogeneity

                                      S>H
H>S                      H>S
                   S>H                  S>H
LC gives smaller segments
            Rheumatism

Soft LC
Soft CCEA
Hard LC
Hard CCEA


            Software journey   Osteoporosis

Soft LC
Soft CCEA
Hard LC
Hard CCEA
MIXED EVIDENCE
Fixed Factors




                         x 10
 100   100   100   100
Sawtooth 2012   what's in a label
Stability: SOFT is better




                                  High confidence
                                  Low confidence

                                 Sim. Index soft > hard
                                 Sim. Index hard > soft

        Strong        Weak
       similarity   similarity
Homogeneity: SOFT is better


                                Scatter hard > soft


                                High confidence
                                Low confidence




       Strong        Weak
      similarity   similarity
Heterogeneity: Hard is better




                                High confidence
                                Low confidence



                                Tot. Sep. soft > hard

       Strong        Weak
      similarity   similarity
Size: Hard is better




                                 High confidence
                                 Low confidence



                                 Uni. dev. soft > hard

        Strong        Weak
       similarity   similarity
HARD ENSEMBLES
GIVE BETTER
BUSINESS
SEGMENTS
Anita Prinzie, Nicole Huyghe
                     anita@solutions2.be
                      www.solutions2.be




        do we cause

risingquestions
References

•   Fred and Jain, Combining Multiple Clustering using Evidence
    Accumulation (2005), IEEE Transactions on Pattern analysis and
    Machine Intelligence, 27(6), 835-850.
•   Lange, T., Roth., V., Braun L. And Buhmann J.M. (2004) , Stability-
    based validation of Clustering Solutions, Neural Computation, 16,
    1299-1323.
•   Haldiki, M.,Vazirgiannis M. and Batistakis, Y. (2000), Quality Scheme
    Assessment in the Clustering Process, Proc. Of the 4th European
    Conference on Principles of Data Mining and Knowledge
    Discovery, 265-276.
•   Hubert, L. And Arabie, P. (1985) Comparing partitions, Journal of
    Classification, 193-218.
•   Nieweglowski, L., CLV package (2007), R software.
•   Martin, A., Quinn, K.M. And Park, J.H., Markov Chain Monte Carlo
    Package (MCMCpack) (2003-2012), R software.

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Sawtooth 2012 what's in a label

  • 1. What’s in a Label? Business value of “soft” vs “hard” cluster ensembles solutions-2 Nicole Huyghe & Anita Prinzie
  • 2. Answers the who and the why
  • 3. Theme 1 Theme 2 Theme 3 ... Theme 9 Theme 10 Cluster Ensemble
  • 5. Stability Integrity Accuracy Size
  • 6. Stability Similarity Index (Lange et al, 2004) indicates the percentage of pairs of observations that belong to the same cluster in both clustering C and clustering C’.
  • 7. Cluster Integrity – Heterogeneity Total separation of clusters: based on the distance between cluster centers
  • 8. Cluster Integrity - Homogeneity Scatter (compactness): average ratio of the cluster variance to the variance of the dataset.
  • 9. Accuracy Reality Prediction 5 5 6 4 6 4 2 1 2 1 3 7 7 3 8 8 9 9 Adjusted Rand Index (Hubert and Arabie, 1985): level of agreement between the predicted segment and the real segment correcting for the expected level of agreement.
  • 10. Size Uniformity deviation: average deviation from each segment from uniform segment size (1/number of segments).
  • 12. Stability Heterogeneity H>S H>S Accuracy Homogeneity S>H H>S H>S S>H S>H
  • 13. LC gives smaller segments Rheumatism Soft LC Soft CCEA Hard LC Hard CCEA Software journey Osteoporosis Soft LC Soft CCEA Hard LC Hard CCEA
  • 15. Fixed Factors x 10 100 100 100 100
  • 17. Stability: SOFT is better High confidence Low confidence Sim. Index soft > hard Sim. Index hard > soft Strong Weak similarity similarity
  • 18. Homogeneity: SOFT is better Scatter hard > soft High confidence Low confidence Strong Weak similarity similarity
  • 19. Heterogeneity: Hard is better High confidence Low confidence Tot. Sep. soft > hard Strong Weak similarity similarity
  • 20. Size: Hard is better High confidence Low confidence Uni. dev. soft > hard Strong Weak similarity similarity
  • 22. Anita Prinzie, Nicole Huyghe anita@solutions2.be www.solutions2.be do we cause risingquestions
  • 23. References • Fred and Jain, Combining Multiple Clustering using Evidence Accumulation (2005), IEEE Transactions on Pattern analysis and Machine Intelligence, 27(6), 835-850. • Lange, T., Roth., V., Braun L. And Buhmann J.M. (2004) , Stability- based validation of Clustering Solutions, Neural Computation, 16, 1299-1323. • Haldiki, M.,Vazirgiannis M. and Batistakis, Y. (2000), Quality Scheme Assessment in the Clustering Process, Proc. Of the 4th European Conference on Principles of Data Mining and Knowledge Discovery, 265-276. • Hubert, L. And Arabie, P. (1985) Comparing partitions, Journal of Classification, 193-218. • Nieweglowski, L., CLV package (2007), R software. • Martin, A., Quinn, K.M. And Park, J.H., Markov Chain Monte Carlo Package (MCMCpack) (2003-2012), R software.