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

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

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.