Scikit learn kmeans with custom definition of inertia?
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I've coded a small clustering algorithm for time signals using kmeans, which works ok (gives acceptable results).
However, kmeans uses the sum of squared differences. I would like to be able to input instead my own measure of difference, but there doesn't seem to be a way provided by the library to do that.
What would be the easiest way to achieve this? Any other python library which may provide me some way to input instead my own function to define the distance? Or I guess I could instead re-implement the algorithm myself, but I'd rather keep the sci-kit one (since they provide functionalities I want to use such as parallel processing).
scikit-learn clustering k-means
asked 7 mins ago
Francis VachonFrancis Vachon
1
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$begingroup$
I've coded a small clustering algorithm for time signals using kmeans, which works ok (gives acceptable results).
However, kmeans uses the sum of squared differences. I would like to be able to input instead my own measure of difference, but there doesn't seem to be a way provided by the library to do that.
What would be the easiest way to achieve this? Any other python library which may provide me some way to input instead my own function to define the distance? Or I guess I could instead re-implement the algorithm myself, but I'd rather keep the sci-kit one (since they provide functionalities I want to use such as parallel processing).
scikit-learn clustering k-means
asked 7 mins ago
Francis VachonFrancis Vachon
1
New contributor
Francis Vachon is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
I've coded a small clustering algorithm for time signals using kmeans, which works ok (gives acceptable results).
However, kmeans uses the sum of squared differences. I would like to be able to input instead my own measure of difference, but there doesn't seem to be a way provided by the library to do that.
What would be the easiest way to achieve this? Any other python library which may provide me some way to input instead my own function to define the distance? Or I guess I could instead re-implement the algorithm myself, but I'd rather keep the sci-kit one (since they provide functionalities I want to use such as parallel processing).
scikit-learn clustering k-means
asked 7 mins ago
Francis VachonFrancis Vachon
1
New contributor
Francis Vachon is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I've coded a small clustering algorithm for time signals using kmeans, which works ok (gives acceptable results).
However, kmeans uses the sum of squared differences. I would like to be able to input instead my own measure of difference, but there doesn't seem to be a way provided by the library to do that.
What would be the easiest way to achieve this? Any other python library which may provide me some way to input instead my own function to define the distance? Or I guess I could instead re-implement the algorithm myself, but I'd rather keep the sci-kit one (since they provide functionalities I want to use such as parallel processing).
scikit-learn clustering k-means
scikit-learn clustering k-means
asked 7 mins ago
Francis VachonFrancis Vachon
1
New contributor
Francis Vachon is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 7 mins ago
Francis VachonFrancis Vachon
1
New contributor
Francis Vachon is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 7 mins ago
Francis VachonFrancis Vachon
1
New contributor
Francis Vachon is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 7 mins ago
Francis VachonFrancis Vachon
1
asked 7 mins ago
Francis VachonFrancis Vachon
1
1
New contributor
Francis Vachon is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Francis Vachon is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Francis Vachon is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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Francis Vachon is a new contributor. Be nice, and check out our Code of Conduct.
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