Calculating clusters based on position ellipses
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I'm looking for pointers to existing algorithms that take a number of position ellipses (centre, axis orientation, majr/minor axes) and cluster them together based on proximity to each other. The traditional way of clustering doesn't tend to use spatial size to inform the clusters.
An alternative way of thinking about it would be to assign a confidence value to the centre coordinates based on the size of the ellipse and use this to inform the cluster assignment.
If anyone can point me in the direction of doing this I would be grateful. I have searched and not found anything directly applicable.
geometry machine-learning clustering
asked Aug 23 at 8:40
sailingH
61
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up vote
1
down vote
favorite
I'm looking for pointers to existing algorithms that take a number of position ellipses (centre, axis orientation, majr/minor axes) and cluster them together based on proximity to each other. The traditional way of clustering doesn't tend to use spatial size to inform the clusters.
An alternative way of thinking about it would be to assign a confidence value to the centre coordinates based on the size of the ellipse and use this to inform the cluster assignment.
If anyone can point me in the direction of doing this I would be grateful. I have searched and not found anything directly applicable.
geometry machine-learning clustering
asked Aug 23 at 8:40
sailingH
61
I think expectation minimization should be possible to adapt to your case. When it determines the likelihood that a given point belongs to a set, it can consider the probability distribution of that data point as well, not only its mean
– Aleksejs Fomins
Aug 23 at 10:59
add a comment |Â
up vote
1
down vote
favorite
up vote
1
down vote
favorite
I'm looking for pointers to existing algorithms that take a number of position ellipses (centre, axis orientation, majr/minor axes) and cluster them together based on proximity to each other. The traditional way of clustering doesn't tend to use spatial size to inform the clusters.
An alternative way of thinking about it would be to assign a confidence value to the centre coordinates based on the size of the ellipse and use this to inform the cluster assignment.
If anyone can point me in the direction of doing this I would be grateful. I have searched and not found anything directly applicable.
geometry machine-learning clustering
asked Aug 23 at 8:40
sailingH
61
I'm looking for pointers to existing algorithms that take a number of position ellipses (centre, axis orientation, majr/minor axes) and cluster them together based on proximity to each other. The traditional way of clustering doesn't tend to use spatial size to inform the clusters.
An alternative way of thinking about it would be to assign a confidence value to the centre coordinates based on the size of the ellipse and use this to inform the cluster assignment.
If anyone can point me in the direction of doing this I would be grateful. I have searched and not found anything directly applicable.
geometry machine-learning clustering
asked Aug 23 at 8:40
sailingH
61
asked Aug 23 at 8:40
sailingH
61
asked Aug 23 at 8:40
sailingH
61
asked Aug 23 at 8:40
sailingH
61
61
I think expectation minimization should be possible to adapt to your case. When it determines the likelihood that a given point belongs to a set, it can consider the probability distribution of that data point as well, not only its mean
– Aleksejs Fomins
Aug 23 at 10:59
add a comment |Â
I think expectation minimization should be possible to adapt to your case. When it determines the likelihood that a given point belongs to a set, it can consider the probability distribution of that data point as well, not only its mean
– Aleksejs Fomins
Aug 23 at 10:59
I think expectation minimization should be possible to adapt to your case. When it determines the likelihood that a given point belongs to a set, it can consider the probability distribution of that data point as well, not only its mean
– Aleksejs Fomins
Aug 23 at 10:59
I think expectation minimization should be possible to adapt to your case. When it determines the likelihood that a given point belongs to a set, it can consider the probability distribution of that data point as well, not only its mean
– Aleksejs Fomins
Aug 23 at 10:59
add a comment |Â
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I think expectation minimization should be possible to adapt to your case. When it determines the likelihood that a given point belongs to a set, it can consider the probability distribution of that data point as well, not only its mean
– Aleksejs Fomins
Aug 23 at 10:59