What clustering algorithm should I use for 2D points? The Next CEO of Stack Overflow2019 Community Moderator ElectionDiscovering dis-associations between periods of time-seriesFast k-means like algorithm for 10^10 points?Algorithm for deriving mutiple clustersExpectation number of points in initial clustering for LSHSeeking Appropriate Clustering AlgorithmClustering documents - how to evaluate results?Data model and algorithm for recommending “related” interestsWhat clustering algorithm is appropriate for clustering paths?Clustering algorithm for a distance matrixCommon cluster points between different runs
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What clustering algorithm should I use for 2D points?
The Next CEO of Stack Overflow2019 Community Moderator ElectionDiscovering dis-associations between periods of time-seriesFast k-means like algorithm for 10^10 points?Algorithm for deriving mutiple clustersExpectation number of points in initial clustering for LSHSeeking Appropriate Clustering AlgorithmClustering documents - how to evaluate results?Data model and algorithm for recommending “related” interestsWhat clustering algorithm is appropriate for clustering paths?Clustering algorithm for a distance matrixCommon cluster points between different runs
$begingroup$
E.g., I've got a list (~50'000) of 2D points and I'd like to run any clusterization algorithms such that it'll automatically find a list of clusters given a diameter.
clustering
asked 1 hour ago
AlexAlex
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add a comment |
$begingroup$
E.g., I've got a list (~50'000) of 2D points and I'd like to run any clusterization algorithms such that it'll automatically find a list of clusters given a diameter.
clustering
asked 1 hour ago
AlexAlex
1
New contributor
Alex is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
$begingroup$
IMO the first thing to do with 2D data is plot the points. In general, a human analyst should be able to spot the interesting patterns. Since you have a decent amount of data, reducing the alpha/transparency of the dots may aid in visualizing density of the points.
$endgroup$
– Mephy
57 mins ago
add a comment |
$begingroup$
E.g., I've got a list (~50'000) of 2D points and I'd like to run any clusterization algorithms such that it'll automatically find a list of clusters given a diameter.
clustering
asked 1 hour ago
AlexAlex
1
New contributor
Alex is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
E.g., I've got a list (~50'000) of 2D points and I'd like to run any clusterization algorithms such that it'll automatically find a list of clusters given a diameter.
clustering
clustering
asked 1 hour ago
AlexAlex
1
New contributor
Alex is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 1 hour ago
AlexAlex
1
New contributor
Alex is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 1 hour ago
AlexAlex
1
New contributor
Alex is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 1 hour ago
AlexAlex
1
asked 1 hour ago
AlexAlex
1
1
New contributor
Alex is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Alex is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Alex is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$begingroup$
IMO the first thing to do with 2D data is plot the points. In general, a human analyst should be able to spot the interesting patterns. Since you have a decent amount of data, reducing the alpha/transparency of the dots may aid in visualizing density of the points.
$endgroup$
– Mephy
57 mins ago
add a comment |
$begingroup$
IMO the first thing to do with 2D data is plot the points. In general, a human analyst should be able to spot the interesting patterns. Since you have a decent amount of data, reducing the alpha/transparency of the dots may aid in visualizing density of the points.
$endgroup$
– Mephy
57 mins ago
$begingroup$
IMO the first thing to do with 2D data is plot the points. In general, a human analyst should be able to spot the interesting patterns. Since you have a decent amount of data, reducing the alpha/transparency of the dots may aid in visualizing density of the points.
$endgroup$
– Mephy
57 mins ago
$begingroup$
IMO the first thing to do with 2D data is plot the points. In general, a human analyst should be able to spot the interesting patterns. Since you have a decent amount of data, reducing the alpha/transparency of the dots may aid in visualizing density of the points.
$endgroup$
– Mephy
57 mins ago
add a comment |
1 Answer
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$begingroup$
You can use DBSCAN or Quadtree for this purpose. These Algos reduce 2D search space so that clustering does not become n*n in complexity.
Explanation of Quadtree :
https://jimkang.com/quadtreevis/
answered 45 mins ago
Shamit VermaShamit Verma
1,039211
$endgroup$
add a comment |
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1 Answer
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1 Answer
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oldest
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$begingroup$
You can use DBSCAN or Quadtree for this purpose. These Algos reduce 2D search space so that clustering does not become n*n in complexity.
Explanation of Quadtree :
https://jimkang.com/quadtreevis/
answered 45 mins ago
Shamit VermaShamit Verma
1,039211
$endgroup$
add a comment |
$begingroup$
You can use DBSCAN or Quadtree for this purpose. These Algos reduce 2D search space so that clustering does not become n*n in complexity.
Explanation of Quadtree :
https://jimkang.com/quadtreevis/
answered 45 mins ago
Shamit VermaShamit Verma
1,039211
$endgroup$
add a comment |
$begingroup$
You can use DBSCAN or Quadtree for this purpose. These Algos reduce 2D search space so that clustering does not become n*n in complexity.
Explanation of Quadtree :
https://jimkang.com/quadtreevis/
answered 45 mins ago
Shamit VermaShamit Verma
1,039211
$endgroup$
You can use DBSCAN or Quadtree for this purpose. These Algos reduce 2D search space so that clustering does not become n*n in complexity.
Explanation of Quadtree :
https://jimkang.com/quadtreevis/
answered 45 mins ago
Shamit VermaShamit Verma
1,039211
answered 45 mins ago
Shamit VermaShamit Verma
1,039211
answered 45 mins ago
Shamit VermaShamit Verma
1,039211
answered 45 mins ago
Shamit VermaShamit Verma
1,039211
1,039211
add a comment |
add a comment |
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$begingroup$
IMO the first thing to do with 2D data is plot the points. In general, a human analyst should be able to spot the interesting patterns. Since you have a decent amount of data, reducing the alpha/transparency of the dots may aid in visualizing density of the points.
$endgroup$
– Mephy
57 mins ago