Sawtooth 2012 what's in a label

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Sawtooth Conference 2012 Orlando - Flordida What's in a label? The business value of hard versus soft clustering by Nicole Huyghe and Anita Prinzie

Negócios Tecnologia Educação

What’s in a Label?
Business value of “soft” vs “hard” cluster ensembles
solutions-2
Nicole Huyghe & Anita Prinzie

Theme 1

Theme 2

Theme 3

...

Theme 9
Theme 10

Cluster
Ensemble

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

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

4. HARD OR SOFT 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).

11. Rheumatism Software journey Osteoporosis

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

14. MIXED EVIDENCE

15. Fixed Factors x 10 100 100 100 100

16.

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

21. HARD ENSEMBLES GIVE BETTER BUSINESS SEGMENTS

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.

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