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Python: how to compare the similarity between clustering using k-means algorithm?
How do I copy a file in Python?What is the difference between Python's list methods append and extend?How can I safely create a nested directory?How can I remove a trailing newline?How do I parse a string to a float or int?How to get the current time in PythonHow can I make a time delay in Python?How do I get the number of elements in a list?How do I concatenate two lists in Python?How do I lowercase a string in Python?
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I have two observations of the same event. Let say X and Y.
I suppose to have nc clusters. I am using sklearn to make the clustering.
x = KMeans(n_clusters=nc).fit_predict(X)
y = KMeans(n_clusters=nc).fit_predict(Y)
is there a measure that allow me to compare x and y: i.e. this measure will be 1 if the clusters x and y are the same.
python cluster-analysis k-means
asked May 13 '16 at 21:29
emaxemax
1,0723 gold badges14 silver badges42 bronze badges
add a comment |
I have two observations of the same event. Let say X and Y.
I suppose to have nc clusters. I am using sklearn to make the clustering.
x = KMeans(n_clusters=nc).fit_predict(X)
y = KMeans(n_clusters=nc).fit_predict(Y)
is there a measure that allow me to compare x and y: i.e. this measure will be 1 if the clusters x and y are the same.
python cluster-analysis k-means
asked May 13 '16 at 21:29
emaxemax
1,0723 gold badges14 silver badges42 bronze badges
add a comment |
I have two observations of the same event. Let say X and Y.
I suppose to have nc clusters. I am using sklearn to make the clustering.
x = KMeans(n_clusters=nc).fit_predict(X)
y = KMeans(n_clusters=nc).fit_predict(Y)
is there a measure that allow me to compare x and y: i.e. this measure will be 1 if the clusters x and y are the same.
python cluster-analysis k-means
asked May 13 '16 at 21:29
emaxemax
1,0723 gold badges14 silver badges42 bronze badges
I have two observations of the same event. Let say X and Y.
I suppose to have nc clusters. I am using sklearn to make the clustering.
x = KMeans(n_clusters=nc).fit_predict(X)
y = KMeans(n_clusters=nc).fit_predict(Y)
is there a measure that allow me to compare x and y: i.e. this measure will be 1 if the clusters x and y are the same.
python cluster-analysis k-means
python cluster-analysis k-means
asked May 13 '16 at 21:29
emaxemax
1,0723 gold badges14 silver badges42 bronze badges
asked May 13 '16 at 21:29
emaxemax
1,0723 gold badges14 silver badges42 bronze badges
asked May 13 '16 at 21:29
emaxemax
1,0723 gold badges14 silver badges42 bronze badges
asked May 13 '16 at 21:29
emaxemax
1,0723 gold badges14 silver badges42 bronze badges
asked May 13 '16 at 21:29
emaxemax
1,0723 gold badges14 silver badges42 bronze badges
1,0723 gold badges14 silver badges42 bronze badges
add a comment |
add a comment |
2 Answers
2
active
oldest
votes
Just extract the cluster centers of your kmeans-objects (see the docs):
x_centers = x.cluster_centers_
y_centers = y.cluster_centers_
The you have to decide which metric you are using to compare these. Keep in mind that the centers are floating-points, the clustering-process is a heuristic and the clustering-process is a random-algorithm. This means, you will get something which interprets as not exactly the same with a high probability, even for cluster-objects trained on the same data.
This link discusses some approaches and the problems.
edited Apr 13 '17 at 12:44
Community♦
11 silver badge
answered May 13 '16 at 21:34
saschasascha
20.1k6 gold badges41 silver badges78 bronze badges
add a comment |
The Rand Index and its adjusted version do this exactly. Two cluster assignments that match (even if the labels themselves, which are treated as arbitrary, are different), get a score of 1. A value of 0 means they don't agree at all. The Adjusted Rand Index uses its baseline as random assignment of points to clusters.
answered Mar 27 at 15:28
Sam A.Sam A.
637 bronze badges
would Adjusted Rand of just Rand make more sense in the OP case?
– serafeim
Jul 30 at 17:50
@serafeim what is OP?
– Sam A.
Aug 11 at 6:40
original post = OP
– serafeim
Aug 12 at 21:49
add a comment |
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2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
Just extract the cluster centers of your kmeans-objects (see the docs):
x_centers = x.cluster_centers_
y_centers = y.cluster_centers_
The you have to decide which metric you are using to compare these. Keep in mind that the centers are floating-points, the clustering-process is a heuristic and the clustering-process is a random-algorithm. This means, you will get something which interprets as not exactly the same with a high probability, even for cluster-objects trained on the same data.
This link discusses some approaches and the problems.
edited Apr 13 '17 at 12:44
Community♦
11 silver badge
answered May 13 '16 at 21:34
saschasascha
20.1k6 gold badges41 silver badges78 bronze badges
add a comment |
Just extract the cluster centers of your kmeans-objects (see the docs):
x_centers = x.cluster_centers_
y_centers = y.cluster_centers_
The you have to decide which metric you are using to compare these. Keep in mind that the centers are floating-points, the clustering-process is a heuristic and the clustering-process is a random-algorithm. This means, you will get something which interprets as not exactly the same with a high probability, even for cluster-objects trained on the same data.
This link discusses some approaches and the problems.
edited Apr 13 '17 at 12:44
Community♦
11 silver badge
answered May 13 '16 at 21:34
saschasascha
20.1k6 gold badges41 silver badges78 bronze badges
add a comment |
Just extract the cluster centers of your kmeans-objects (see the docs):
x_centers = x.cluster_centers_
y_centers = y.cluster_centers_
The you have to decide which metric you are using to compare these. Keep in mind that the centers are floating-points, the clustering-process is a heuristic and the clustering-process is a random-algorithm. This means, you will get something which interprets as not exactly the same with a high probability, even for cluster-objects trained on the same data.
This link discusses some approaches and the problems.
edited Apr 13 '17 at 12:44
Community♦
11 silver badge
answered May 13 '16 at 21:34
saschasascha
20.1k6 gold badges41 silver badges78 bronze badges
Just extract the cluster centers of your kmeans-objects (see the docs):
x_centers = x.cluster_centers_
y_centers = y.cluster_centers_
The you have to decide which metric you are using to compare these. Keep in mind that the centers are floating-points, the clustering-process is a heuristic and the clustering-process is a random-algorithm. This means, you will get something which interprets as not exactly the same with a high probability, even for cluster-objects trained on the same data.
This link discusses some approaches and the problems.
edited Apr 13 '17 at 12:44
Community♦
11 silver badge
answered May 13 '16 at 21:34
saschasascha
20.1k6 gold badges41 silver badges78 bronze badges
edited Apr 13 '17 at 12:44
Community♦
11 silver badge
edited Apr 13 '17 at 12:44
Community♦
11 silver badge
edited Apr 13 '17 at 12:44
Community♦
11 silver badge
11 silver badge
answered May 13 '16 at 21:34
saschasascha
20.1k6 gold badges41 silver badges78 bronze badges
answered May 13 '16 at 21:34
saschasascha
20.1k6 gold badges41 silver badges78 bronze badges
answered May 13 '16 at 21:34
saschasascha
20.1k6 gold badges41 silver badges78 bronze badges
20.1k6 gold badges41 silver badges78 bronze badges
add a comment |
add a comment |
The Rand Index and its adjusted version do this exactly. Two cluster assignments that match (even if the labels themselves, which are treated as arbitrary, are different), get a score of 1. A value of 0 means they don't agree at all. The Adjusted Rand Index uses its baseline as random assignment of points to clusters.
answered Mar 27 at 15:28
Sam A.Sam A.
637 bronze badges
would Adjusted Rand of just Rand make more sense in the OP case?
– serafeim
Jul 30 at 17:50
@serafeim what is OP?
– Sam A.
Aug 11 at 6:40
original post = OP
– serafeim
Aug 12 at 21:49
add a comment |
The Rand Index and its adjusted version do this exactly. Two cluster assignments that match (even if the labels themselves, which are treated as arbitrary, are different), get a score of 1. A value of 0 means they don't agree at all. The Adjusted Rand Index uses its baseline as random assignment of points to clusters.
answered Mar 27 at 15:28
Sam A.Sam A.
637 bronze badges
would Adjusted Rand of just Rand make more sense in the OP case?
– serafeim
Jul 30 at 17:50
@serafeim what is OP?
– Sam A.
Aug 11 at 6:40
original post = OP
– serafeim
Aug 12 at 21:49
add a comment |
The Rand Index and its adjusted version do this exactly. Two cluster assignments that match (even if the labels themselves, which are treated as arbitrary, are different), get a score of 1. A value of 0 means they don't agree at all. The Adjusted Rand Index uses its baseline as random assignment of points to clusters.
answered Mar 27 at 15:28
Sam A.Sam A.
637 bronze badges
The Rand Index and its adjusted version do this exactly. Two cluster assignments that match (even if the labels themselves, which are treated as arbitrary, are different), get a score of 1. A value of 0 means they don't agree at all. The Adjusted Rand Index uses its baseline as random assignment of points to clusters.
answered Mar 27 at 15:28
Sam A.Sam A.
637 bronze badges
answered Mar 27 at 15:28
Sam A.Sam A.
637 bronze badges
answered Mar 27 at 15:28
Sam A.Sam A.
637 bronze badges
answered Mar 27 at 15:28
Sam A.Sam A.
637 bronze badges
637 bronze badges
would Adjusted Rand of just Rand make more sense in the OP case?
– serafeim
Jul 30 at 17:50
@serafeim what is OP?
– Sam A.
Aug 11 at 6:40
original post = OP
– serafeim
Aug 12 at 21:49
add a comment |
would Adjusted Rand of just Rand make more sense in the OP case?
– serafeim
Jul 30 at 17:50
@serafeim what is OP?
– Sam A.
Aug 11 at 6:40
original post = OP
– serafeim
Aug 12 at 21:49
would Adjusted Rand of just Rand make more sense in the OP case?
– serafeim
Jul 30 at 17:50
would Adjusted Rand of just Rand make more sense in the OP case?
– serafeim
Jul 30 at 17:50
@serafeim what is OP?
– Sam A.
Aug 11 at 6:40
@serafeim what is OP?
– Sam A.
Aug 11 at 6:40
original post = OP
– serafeim
Aug 12 at 21:49
original post = OP
– serafeim
Aug 12 at 21:49
add a comment |
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