How to use precomputed distance matrix and min_sample for DBSCAN clustering method?Clustering not producing even clustersJoint spatial clustering: How to force clusters to minimally contain datapoints from all datasetsKnn distance plot for determining eps of DBSCANClustering with restrictions - Silhouette and C index metricsCan you l2 normalize word2vec vectors for density clustering?t-SNE plotting DBSCAN clustering results very scattered issueClustering with multiple distance measuresIs my data good for (DBSCAN) clustering?Is there an oriented clustering algorithm?Similarity measure before and after dimensionality reduction or clustering
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How to use precomputed distance matrix and min_sample for DBSCAN clustering method?
Clustering not producing even clustersJoint spatial clustering: How to force clusters to minimally contain datapoints from all datasetsKnn distance plot for determining eps of DBSCANClustering with restrictions - Silhouette and C index metricsCan you l2 normalize word2vec vectors for density clustering?t-SNE plotting DBSCAN clustering results very scattered issueClustering with multiple distance measuresIs my data good for (DBSCAN) clustering?Is there an oriented clustering algorithm?Similarity measure before and after dimensionality reduction or clustering
$begingroup$
I want to perform DBSCAN on my datapoints, but I don't have access to the data, I just have the pairwise distance of datapoints. Additionally, I have no idea about the number of clusters but I do want that each cluster contains at least 40 data points. Does DBSCAN work with these conditions? For instance, can I have something like this? Or is more information needed? I want to emphasize that I have computed the pairwise distance and this is not the result of Euclidean or some other method.
from sklearn.cluster import DBSCAN
db = DBSCAN(min_samples=40, metric="precomputed")
y_db = db.fit_predict(my_pairwise_distance_matrix)
I am not sure what is eps parameter of DBSCAN(). How should I set that?
machine-learning clustering scikit-learn dbscan
edited Jul 14 '17 at 8:35
tuomastik
771520
asked Jul 14 '17 at 0:30
ArianiAriani
215
$endgroup$
bumped to the homepage by Community♦ 2 mins 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 want to perform DBSCAN on my datapoints, but I don't have access to the data, I just have the pairwise distance of datapoints. Additionally, I have no idea about the number of clusters but I do want that each cluster contains at least 40 data points. Does DBSCAN work with these conditions? For instance, can I have something like this? Or is more information needed? I want to emphasize that I have computed the pairwise distance and this is not the result of Euclidean or some other method.
from sklearn.cluster import DBSCAN
db = DBSCAN(min_samples=40, metric="precomputed")
y_db = db.fit_predict(my_pairwise_distance_matrix)
I am not sure what is eps parameter of DBSCAN(). How should I set that?
machine-learning clustering scikit-learn dbscan
edited Jul 14 '17 at 8:35
tuomastik
771520
asked Jul 14 '17 at 0:30
ArianiAriani
215
$endgroup$
bumped to the homepage by Community♦ 2 mins 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 want to perform DBSCAN on my datapoints, but I don't have access to the data, I just have the pairwise distance of datapoints. Additionally, I have no idea about the number of clusters but I do want that each cluster contains at least 40 data points. Does DBSCAN work with these conditions? For instance, can I have something like this? Or is more information needed? I want to emphasize that I have computed the pairwise distance and this is not the result of Euclidean or some other method.
from sklearn.cluster import DBSCAN
db = DBSCAN(min_samples=40, metric="precomputed")
y_db = db.fit_predict(my_pairwise_distance_matrix)
I am not sure what is eps parameter of DBSCAN(). How should I set that?
machine-learning clustering scikit-learn dbscan
edited Jul 14 '17 at 8:35
tuomastik
771520
asked Jul 14 '17 at 0:30
ArianiAriani
215
$endgroup$
I want to perform DBSCAN on my datapoints, but I don't have access to the data, I just have the pairwise distance of datapoints. Additionally, I have no idea about the number of clusters but I do want that each cluster contains at least 40 data points. Does DBSCAN work with these conditions? For instance, can I have something like this? Or is more information needed? I want to emphasize that I have computed the pairwise distance and this is not the result of Euclidean or some other method.
from sklearn.cluster import DBSCAN
db = DBSCAN(min_samples=40, metric="precomputed")
y_db = db.fit_predict(my_pairwise_distance_matrix)
I am not sure what is eps parameter of DBSCAN(). How should I set that?
machine-learning clustering scikit-learn dbscan
machine-learning clustering scikit-learn dbscan
edited Jul 14 '17 at 8:35
tuomastik
771520
asked Jul 14 '17 at 0:30
ArianiAriani
215
edited Jul 14 '17 at 8:35
tuomastik
771520
asked Jul 14 '17 at 0:30
ArianiAriani
215
edited Jul 14 '17 at 8:35
tuomastik
771520
edited Jul 14 '17 at 8:35
tuomastik
771520
edited Jul 14 '17 at 8:35
tuomastik
771520
771520
asked Jul 14 '17 at 0:30
ArianiAriani
215
asked Jul 14 '17 at 0:30
ArianiAriani
215
asked Jul 14 '17 at 0:30
ArianiAriani
215
215
bumped to the homepage by Community♦ 2 mins 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♦ 2 mins 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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$begingroup$
DBSCAN does not guarantee a minimum cluster size. There are known situations, c.f. Wikipedia, where a cluster can have fewer than "minPts" points. Furthermore, it has the concept of noise: points that do not have enough neighbors.
For epsilon, also see the Wikipedia article. As you don't specify the number of clusters, this parameter is what mostly controls how many clusters you get. Set it to 0, and everything will be noise. Set it to the maximum distance, and everything will be in one cluster.
Really read the article. It's about density, not about cluster sizes.
answered Jul 14 '17 at 6:59
Anony-MousseAnony-Mousse
5,340625
$endgroup$
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$begingroup$
DBSCAN does not guarantee a minimum cluster size. There are known situations, c.f. Wikipedia, where a cluster can have fewer than "minPts" points. Furthermore, it has the concept of noise: points that do not have enough neighbors.
For epsilon, also see the Wikipedia article. As you don't specify the number of clusters, this parameter is what mostly controls how many clusters you get. Set it to 0, and everything will be noise. Set it to the maximum distance, and everything will be in one cluster.
Really read the article. It's about density, not about cluster sizes.
answered Jul 14 '17 at 6:59
Anony-MousseAnony-Mousse
5,340625
$endgroup$
add a comment |
$begingroup$
DBSCAN does not guarantee a minimum cluster size. There are known situations, c.f. Wikipedia, where a cluster can have fewer than "minPts" points. Furthermore, it has the concept of noise: points that do not have enough neighbors.
For epsilon, also see the Wikipedia article. As you don't specify the number of clusters, this parameter is what mostly controls how many clusters you get. Set it to 0, and everything will be noise. Set it to the maximum distance, and everything will be in one cluster.
Really read the article. It's about density, not about cluster sizes.
answered Jul 14 '17 at 6:59
Anony-MousseAnony-Mousse
5,340625
$endgroup$
add a comment |
$begingroup$
DBSCAN does not guarantee a minimum cluster size. There are known situations, c.f. Wikipedia, where a cluster can have fewer than "minPts" points. Furthermore, it has the concept of noise: points that do not have enough neighbors.
For epsilon, also see the Wikipedia article. As you don't specify the number of clusters, this parameter is what mostly controls how many clusters you get. Set it to 0, and everything will be noise. Set it to the maximum distance, and everything will be in one cluster.
Really read the article. It's about density, not about cluster sizes.
answered Jul 14 '17 at 6:59
Anony-MousseAnony-Mousse
5,340625
$endgroup$
DBSCAN does not guarantee a minimum cluster size. There are known situations, c.f. Wikipedia, where a cluster can have fewer than "minPts" points. Furthermore, it has the concept of noise: points that do not have enough neighbors.
For epsilon, also see the Wikipedia article. As you don't specify the number of clusters, this parameter is what mostly controls how many clusters you get. Set it to 0, and everything will be noise. Set it to the maximum distance, and everything will be in one cluster.
Really read the article. It's about density, not about cluster sizes.
answered Jul 14 '17 at 6:59
Anony-MousseAnony-Mousse
5,340625
answered Jul 14 '17 at 6:59
Anony-MousseAnony-Mousse
5,340625
answered Jul 14 '17 at 6:59
Anony-MousseAnony-Mousse
5,340625
answered Jul 14 '17 at 6:59
Anony-MousseAnony-Mousse
5,340625
5,340625
add a comment |
add a comment |
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