How to find unknown number of clusters in circular data?
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I have some 1 dimensional data. Each record in the data is a specific time of the day. In order to cluster it I projected the data onto a circle of radius 1 unit. Now I need to find clusters in this data. The number of clusters are unknown and it is preferred to find clusters with high density of records in them. By density I mean that a large volume of records should be packed in a small space.
How should I go about finding clusters in the above mentioned data?
clustering unsupervised-learning
asked Jun 16 '18 at 11:35
SidSid
1011
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bumped to the homepage by Community♦ 1 min ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
$begingroup$
I have some 1 dimensional data. Each record in the data is a specific time of the day. In order to cluster it I projected the data onto a circle of radius 1 unit. Now I need to find clusters in this data. The number of clusters are unknown and it is preferred to find clusters with high density of records in them. By density I mean that a large volume of records should be packed in a small space.
How should I go about finding clusters in the above mentioned data?
clustering unsupervised-learning
asked Jun 16 '18 at 11:35
SidSid
1011
$endgroup$
bumped to the homepage by Community♦ 1 min ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
$begingroup$
I have some 1 dimensional data. Each record in the data is a specific time of the day. In order to cluster it I projected the data onto a circle of radius 1 unit. Now I need to find clusters in this data. The number of clusters are unknown and it is preferred to find clusters with high density of records in them. By density I mean that a large volume of records should be packed in a small space.
How should I go about finding clusters in the above mentioned data?
clustering unsupervised-learning
asked Jun 16 '18 at 11:35
SidSid
1011
$endgroup$
I have some 1 dimensional data. Each record in the data is a specific time of the day. In order to cluster it I projected the data onto a circle of radius 1 unit. Now I need to find clusters in this data. The number of clusters are unknown and it is preferred to find clusters with high density of records in them. By density I mean that a large volume of records should be packed in a small space.
How should I go about finding clusters in the above mentioned data?
clustering unsupervised-learning
clustering unsupervised-learning
asked Jun 16 '18 at 11:35
SidSid
1011
asked Jun 16 '18 at 11:35
SidSid
1011
asked Jun 16 '18 at 11:35
SidSid
1011
asked Jun 16 '18 at 11:35
SidSid
1011
asked Jun 16 '18 at 11:35
SidSid
1011
1011
bumped to the homepage by Community♦ 1 min ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
bumped to the homepage by Community♦ 1 min ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
add a comment |
1 Answer
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Instead of projecting into the circle and thus making your problem 2d, why don't you just use a cyclic distance measure?
This problem should be straightforward by doing kernel density estimation on the (cyclic) time of day. Then find the peaks, which are your clusters.
answered Jun 19 '18 at 7:06
Anony-MousseAnony-Mousse
5,195625
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$begingroup$
Instead of projecting into the circle and thus making your problem 2d, why don't you just use a cyclic distance measure?
This problem should be straightforward by doing kernel density estimation on the (cyclic) time of day. Then find the peaks, which are your clusters.
answered Jun 19 '18 at 7:06
Anony-MousseAnony-Mousse
5,195625
$endgroup$
add a comment |
$begingroup$
Instead of projecting into the circle and thus making your problem 2d, why don't you just use a cyclic distance measure?
This problem should be straightforward by doing kernel density estimation on the (cyclic) time of day. Then find the peaks, which are your clusters.
answered Jun 19 '18 at 7:06
Anony-MousseAnony-Mousse
5,195625
$endgroup$
add a comment |
$begingroup$
Instead of projecting into the circle and thus making your problem 2d, why don't you just use a cyclic distance measure?
This problem should be straightforward by doing kernel density estimation on the (cyclic) time of day. Then find the peaks, which are your clusters.
answered Jun 19 '18 at 7:06
Anony-MousseAnony-Mousse
5,195625
$endgroup$
Instead of projecting into the circle and thus making your problem 2d, why don't you just use a cyclic distance measure?
This problem should be straightforward by doing kernel density estimation on the (cyclic) time of day. Then find the peaks, which are your clusters.
answered Jun 19 '18 at 7:06
Anony-MousseAnony-Mousse
5,195625
answered Jun 19 '18 at 7:06
Anony-MousseAnony-Mousse
5,195625
answered Jun 19 '18 at 7:06
Anony-MousseAnony-Mousse
5,195625
answered Jun 19 '18 at 7:06
Anony-MousseAnony-Mousse
5,195625
5,195625
add a comment |
add a comment |
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