It%u2019s going to be easy question to you. I%u2019ve started to interest in
model-based clustering.
Adrian E. Raftery %u201CRecent Advances in Model-Based Clustering: Image
Segmentation and Variable Selection%u201D (www.stat.washington.edu/Raftery)
showed that we can compare different classification methods using BIC statistic.
For %u201Cdiabetes%u201D dataset the best model is VVV model with 3 classes- for
this model the BIC curve reaches the highest value and the error rate=12%
BIC curve for EII model %u2248k-means is much under the VVV model curve and the
error rate equals 18%, so k-means (EII) is worse then VVV, what%u2019s clear
for me.
I would like to apply model-based to economic data set (GDP, life expectancy
data of UE countries), because I%u2019m PhD student of University of Economics
in Poland.
Using this data (standardized) I get the best model EEV (2 classes), EII
(k-means) curve is under EEVcurve what suggests that k-means is worse then EEV,
but class error for EII equals 0 and for EEV= 6% (and more for another
variables), why?
Even applying %u201Ciris%u201D data we get lower class error for EII model (10%)
than for VEV (33%) for 2 classes, in spite of another models curve are above
EII model at the BIC plot.
For this data BIC doesn%u2019t choose the right number of clusters- it chooses
VEV for 2 clusters while the right number of classes, given in column five
equals 3.
When model-based clustering (for which data sets, are there any special type of
data) is better than k-means (kmeans), hierarchical clustering (hclust)?
I%u2019m looking forward to hearing from you.
Best regards,
Ewa
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