Elbow Test using AIC/BIC for identifying number of clusters using GMM
Multi tool use
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How to select number of clusters using GMM when the elbow test (AIC/BIC vs n_components) results in a graph like this?
aic bic generalized-moments
edited Sep 25 at 9:13
Ferdi
3,39342151
asked Sep 25 at 8:29
psangam
332
add a comment |Â
up vote
6
down vote
favorite
How to select number of clusters using GMM when the elbow test (AIC/BIC vs n_components) results in a graph like this?
aic bic generalized-moments
edited Sep 25 at 9:13
Ferdi
3,39342151
asked Sep 25 at 8:29
psangam
332
6 seems to be what you are looking for, if you are looking for a test there are a few, for ex. using clusGap statistic.
– user2974951
Sep 25 at 8:51
add a comment |Â
up vote
6
down vote
favorite
up vote
6
down vote
favorite
How to select number of clusters using GMM when the elbow test (AIC/BIC vs n_components) results in a graph like this?
aic bic generalized-moments
edited Sep 25 at 9:13
Ferdi
3,39342151
asked Sep 25 at 8:29
psangam
332
How to select number of clusters using GMM when the elbow test (AIC/BIC vs n_components) results in a graph like this?
aic bic generalized-moments
aic bic generalized-moments
edited Sep 25 at 9:13
Ferdi
3,39342151
asked Sep 25 at 8:29
psangam
332
edited Sep 25 at 9:13
Ferdi
3,39342151
asked Sep 25 at 8:29
psangam
332
edited Sep 25 at 9:13
Ferdi
3,39342151
edited Sep 25 at 9:13
Ferdi
3,39342151
edited Sep 25 at 9:13
Ferdi
3,39342151
3,39342151
asked Sep 25 at 8:29
psangam
332
asked Sep 25 at 8:29
psangam
332
asked Sep 25 at 8:29
psangam
332
332
6 seems to be what you are looking for, if you are looking for a test there are a few, for ex. using clusGap statistic.
– user2974951
Sep 25 at 8:51
add a comment |Â
6 seems to be what you are looking for, if you are looking for a test there are a few, for ex. using clusGap statistic.
– user2974951
Sep 25 at 8:51
6 seems to be what you are looking for, if you are looking for a test there are a few, for ex. using clusGap statistic.
– user2974951
Sep 25 at 8:51
6 seems to be what you are looking for, if you are looking for a test there are a few, for ex. using clusGap statistic.
– user2974951
Sep 25 at 8:51
add a comment |Â
1 Answer
1
active
oldest
votes
up vote
4
down vote
accepted
Welcome to CV!
This plot shows how the AIC and BIC change as a function of the number of clusters. While the AIC continuous to decrease with a larger number of clusters, you can see that the BIC stops decreasing after $k=6$ clusters. For this reason, you could choose $k = 6$.
Another way to choose the 'best' number of clusters is by considering the elbow(s) of the figure. The elbow of a function is a point after which the decrease becomes notably smaller. An elbow is a heuristic, so there is no exact way to determine which value best describes this point. For example, one could argue that the AIC & BIC both stop decreasing as much after $k = 5$ clusters, while someone else might argue that this is after $k = 6$ clusters. You could even argue that the biggest decrease has already happened after $k = 2$ clusters.
Lastly, you don't have to choose any number of clusters just because AIC/BIC/whatever suggested you do so. If you have some a priori reason to assume that there should be $k = 3$ clusters, then that might be a better choice.
In short: An elbow in this context is a heuristic guide to decide the number of clusters if you have no other reason to assume a certain number of clusters.
answered Sep 25 at 8:53
Frans Rodenburg
3,107426
add a comment |Â
1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
up vote
4
down vote
accepted
Welcome to CV!
This plot shows how the AIC and BIC change as a function of the number of clusters. While the AIC continuous to decrease with a larger number of clusters, you can see that the BIC stops decreasing after $k=6$ clusters. For this reason, you could choose $k = 6$.
Another way to choose the 'best' number of clusters is by considering the elbow(s) of the figure. The elbow of a function is a point after which the decrease becomes notably smaller. An elbow is a heuristic, so there is no exact way to determine which value best describes this point. For example, one could argue that the AIC & BIC both stop decreasing as much after $k = 5$ clusters, while someone else might argue that this is after $k = 6$ clusters. You could even argue that the biggest decrease has already happened after $k = 2$ clusters.
Lastly, you don't have to choose any number of clusters just because AIC/BIC/whatever suggested you do so. If you have some a priori reason to assume that there should be $k = 3$ clusters, then that might be a better choice.
In short: An elbow in this context is a heuristic guide to decide the number of clusters if you have no other reason to assume a certain number of clusters.
answered Sep 25 at 8:53
Frans Rodenburg
3,107426
add a comment |Â
up vote
4
down vote
accepted
Welcome to CV!
This plot shows how the AIC and BIC change as a function of the number of clusters. While the AIC continuous to decrease with a larger number of clusters, you can see that the BIC stops decreasing after $k=6$ clusters. For this reason, you could choose $k = 6$.
Another way to choose the 'best' number of clusters is by considering the elbow(s) of the figure. The elbow of a function is a point after which the decrease becomes notably smaller. An elbow is a heuristic, so there is no exact way to determine which value best describes this point. For example, one could argue that the AIC & BIC both stop decreasing as much after $k = 5$ clusters, while someone else might argue that this is after $k = 6$ clusters. You could even argue that the biggest decrease has already happened after $k = 2$ clusters.
Lastly, you don't have to choose any number of clusters just because AIC/BIC/whatever suggested you do so. If you have some a priori reason to assume that there should be $k = 3$ clusters, then that might be a better choice.
In short: An elbow in this context is a heuristic guide to decide the number of clusters if you have no other reason to assume a certain number of clusters.
answered Sep 25 at 8:53
Frans Rodenburg
3,107426
add a comment |Â
up vote
4
down vote
accepted
up vote
4
down vote
accepted
Welcome to CV!
This plot shows how the AIC and BIC change as a function of the number of clusters. While the AIC continuous to decrease with a larger number of clusters, you can see that the BIC stops decreasing after $k=6$ clusters. For this reason, you could choose $k = 6$.
Another way to choose the 'best' number of clusters is by considering the elbow(s) of the figure. The elbow of a function is a point after which the decrease becomes notably smaller. An elbow is a heuristic, so there is no exact way to determine which value best describes this point. For example, one could argue that the AIC & BIC both stop decreasing as much after $k = 5$ clusters, while someone else might argue that this is after $k = 6$ clusters. You could even argue that the biggest decrease has already happened after $k = 2$ clusters.
Lastly, you don't have to choose any number of clusters just because AIC/BIC/whatever suggested you do so. If you have some a priori reason to assume that there should be $k = 3$ clusters, then that might be a better choice.
In short: An elbow in this context is a heuristic guide to decide the number of clusters if you have no other reason to assume a certain number of clusters.
answered Sep 25 at 8:53
Frans Rodenburg
3,107426
Welcome to CV!
This plot shows how the AIC and BIC change as a function of the number of clusters. While the AIC continuous to decrease with a larger number of clusters, you can see that the BIC stops decreasing after $k=6$ clusters. For this reason, you could choose $k = 6$.
Another way to choose the 'best' number of clusters is by considering the elbow(s) of the figure. The elbow of a function is a point after which the decrease becomes notably smaller. An elbow is a heuristic, so there is no exact way to determine which value best describes this point. For example, one could argue that the AIC & BIC both stop decreasing as much after $k = 5$ clusters, while someone else might argue that this is after $k = 6$ clusters. You could even argue that the biggest decrease has already happened after $k = 2$ clusters.
Lastly, you don't have to choose any number of clusters just because AIC/BIC/whatever suggested you do so. If you have some a priori reason to assume that there should be $k = 3$ clusters, then that might be a better choice.
In short: An elbow in this context is a heuristic guide to decide the number of clusters if you have no other reason to assume a certain number of clusters.
answered Sep 25 at 8:53
Frans Rodenburg
3,107426
answered Sep 25 at 8:53
Frans Rodenburg
3,107426
answered Sep 25 at 8:53
Frans Rodenburg
3,107426
answered Sep 25 at 8:53
Frans Rodenburg
3,107426
3,107426
add a comment |Â
add a comment |Â
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6 seems to be what you are looking for, if you are looking for a test there are a few, for ex. using clusGap statistic.
– user2974951
Sep 25 at 8:51