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Implement @turf/clusters-dbscan
module
#812
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d0d8970
Implement `@turf/clusters-distance` module
DenisCarriere 24cf66d
Update yarn lock
DenisCarriere 8669d30
Update debug file
DenisCarriere e5fb827
simplified calculation (run almost x2 faster);
stebogit 17ca549
Convert index.js to ES5
DenisCarriere ed0e48a
Publish new clusters-distance approach
DenisCarriere 88d3953
Add minPoints to tests param
DenisCarriere cf53d66
Update Typescript tests
DenisCarriere 4636f3a
Merge branch 'master' into clusters-distance
DenisCarriere 0893af2
added units parameter; added parameters validation and throw tests
stebogit 359815a
Suggested DBSCAN implementation for `@turf/clusters-distance` (#840)
stebogit a14fa69
Update Typescript Defintion (3 outputs)
DenisCarriere 0a60b90
Merge branch 'master' into clusters-distance
DenisCarriere 144ef79
Merge branch 'clusters-distance' of /~https://github.com/Turfjs/turf in…
stebogit ef76c37
Add geokdbush as reference to repo
DenisCarriere 7bee88f
Single line JSDocs param
DenisCarriere 3eb3d66
Fix tests (results.points)
DenisCarriere 07dea46
Make both index + index.geokdbush work
DenisCarriere 62e4e91
Place Geokdbush to DevDependencies
DenisCarriere 63275fc
Fix noise issue
DenisCarriere 82485d8
Prevent input mutation & add edges
DenisCarriere 8a45889
Major changes
DenisCarriere c2be7be
Create a set of clusters to colorize
DenisCarriere 01e92de
Define edges with cross
DenisCarriere 01bc50b
Add CentroidFromProperty to tests
DenisCarriere d3a3166
Updates based on @stebogit comments
DenisCarriere 28436e7
Update Readme
DenisCarriere d926702
Update benchmark results & drop geokdbush
DenisCarriere 7fe4acf
Add noisePoint.properties fallback incase no props
DenisCarriere 67e9071
Added Array of Features handling
DenisCarriere e7fb4a8
Update library to clusters-dbscan
DenisCarriere 9bdd634
Rename folder to clusters-dbscan
DenisCarriere af57608
Update readme to clusters-dbscan
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Add CentroidFromProperty to tests
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commit 01bc50b106ab57dcc12cf7d1d8830ed07d0a27d0
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@stebogit Added back your centroids points 😄
Would be interesting to know the benchmark results on that
centroidFromProperty
method (i'm sure it's really fast... just quickly scans the FeatureCollection once and then applies clusters based on those bins).There was a problem hiding this comment.
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Mmmh... in density clustering the centroids are less useful/identifying/important than in k-means, I guess. 🤔
But I might be wrong.
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Oh, I see now, this is only in
test.js
. Good 👍There was a problem hiding this comment.
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Yes! :) only for
test.js
This wherecentroidByProperty
module would be used... we wouldn't apply this directly in the modules, but for visual purposes.Also this can be applied against Polygons or any Geometry Types, finding the "centroid" of stuff based on properties is quite useful.