How to give a higher importance to certain features in a (k-means) clustering model? Announcing the arrival of Valued Associate #679: Cesar Manara Planned maintenance scheduled April 23, 2019 at 23:30 UTC (7:30pm US/Eastern) 2019 Moderator Election Q&A - Questionnaire 2019 Community Moderator Election ResultsK-Means clustering for mixed numeric and categorical dataHow to visualize (make plot) of regression output against categorical input variable?Calculate feature weight vector for one-hot-encoded data frame in RModel-agnostic variable importance metricData scaling before PCA: how to deal with categorical values?Extracting useful features for k-means clusteringHow to deal with Nominal categorical with label encoding?How to deal with a potencially multiple categorical variablePython - Create many dummy variables from one text variable?How to validate clusters after calculating Gower distances and Ward's clustering in RPerform k-means clustering over multiple columns
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How to give a higher importance to certain features in a (k-means) clustering model?
Announcing the arrival of Valued Associate #679: Cesar Manara
Planned maintenance scheduled April 23, 2019 at 23:30 UTC (7:30pm US/Eastern)
2019 Moderator Election Q&A - Questionnaire
2019 Community Moderator Election ResultsK-Means clustering for mixed numeric and categorical dataHow to visualize (make plot) of regression output against categorical input variable?Calculate feature weight vector for one-hot-encoded data frame in RModel-agnostic variable importance metricData scaling before PCA: how to deal with categorical values?Extracting useful features for k-means clusteringHow to deal with Nominal categorical with label encoding?How to deal with a potencially multiple categorical variablePython - Create many dummy variables from one text variable?How to validate clusters after calculating Gower distances and Ward's clustering in RPerform k-means clustering over multiple columns
$begingroup$
I am clustering data with numeric and categorical variables. To process the categorical variables for the cluster model, I create dummy variables. However, I feel like this results in a higher importance for these dummy variables because multiple dummy variables represent one categorical variable.
For example, I have a categorical variable Airport that will result in multiple dummy variables: LAX, JFK, MIA and BOS. Now suppose I also have a numeric Temperature variable. I also scale all variables to be between 0 and 1. Now my Airport variable seems to be 4 times more important than the Temperature variable, and the clusters will be mostly based on the Airport variable.
My problem is that I want all variables to have the same importance. Is there a way to do this? I was thinking of scaling the variables in a different way but I don't know how to scale them in order to give them the same importance.
machine-learning clustering feature-scaling dummy-variables
asked Apr 16 at 8:33
EvaEva
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add a comment |
$begingroup$
I am clustering data with numeric and categorical variables. To process the categorical variables for the cluster model, I create dummy variables. However, I feel like this results in a higher importance for these dummy variables because multiple dummy variables represent one categorical variable.
For example, I have a categorical variable Airport that will result in multiple dummy variables: LAX, JFK, MIA and BOS. Now suppose I also have a numeric Temperature variable. I also scale all variables to be between 0 and 1. Now my Airport variable seems to be 4 times more important than the Temperature variable, and the clusters will be mostly based on the Airport variable.
My problem is that I want all variables to have the same importance. Is there a way to do this? I was thinking of scaling the variables in a different way but I don't know how to scale them in order to give them the same importance.
machine-learning clustering feature-scaling dummy-variables
asked Apr 16 at 8:33
EvaEva
363
New contributor
Eva 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 am clustering data with numeric and categorical variables. To process the categorical variables for the cluster model, I create dummy variables. However, I feel like this results in a higher importance for these dummy variables because multiple dummy variables represent one categorical variable.
For example, I have a categorical variable Airport that will result in multiple dummy variables: LAX, JFK, MIA and BOS. Now suppose I also have a numeric Temperature variable. I also scale all variables to be between 0 and 1. Now my Airport variable seems to be 4 times more important than the Temperature variable, and the clusters will be mostly based on the Airport variable.
My problem is that I want all variables to have the same importance. Is there a way to do this? I was thinking of scaling the variables in a different way but I don't know how to scale them in order to give them the same importance.
machine-learning clustering feature-scaling dummy-variables
asked Apr 16 at 8:33
EvaEva
363
New contributor
Eva is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I am clustering data with numeric and categorical variables. To process the categorical variables for the cluster model, I create dummy variables. However, I feel like this results in a higher importance for these dummy variables because multiple dummy variables represent one categorical variable.
For example, I have a categorical variable Airport that will result in multiple dummy variables: LAX, JFK, MIA and BOS. Now suppose I also have a numeric Temperature variable. I also scale all variables to be between 0 and 1. Now my Airport variable seems to be 4 times more important than the Temperature variable, and the clusters will be mostly based on the Airport variable.
My problem is that I want all variables to have the same importance. Is there a way to do this? I was thinking of scaling the variables in a different way but I don't know how to scale them in order to give them the same importance.
machine-learning clustering feature-scaling dummy-variables
machine-learning clustering feature-scaling dummy-variables
asked Apr 16 at 8:33
EvaEva
363
New contributor
Eva is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked Apr 16 at 8:33
EvaEva
363
New contributor
Eva is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked Apr 16 at 8:33
EvaEva
363
New contributor
Eva is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked Apr 16 at 8:33
EvaEva
363
asked Apr 16 at 8:33
EvaEva
363
363
New contributor
Eva is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Eva is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Eva is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
add a comment |
add a comment |
3 Answers
3
active
oldest
votes
$begingroup$
You cannot really use k-means clustering if your data contains categorical variables since k-means uses Euclidian distance which will not make a lot of sense with dummy-variables. Check out the answers to this similar question.
I would suggest, you switch to k-modes for your clustering algorithm. You will find good implementations both for Python and R.
answered Apr 16 at 9:15
georg_ungeorg_un
318111
$endgroup$
add a comment |
$begingroup$
Clearly the objective function uses a sum over the features.
So if you want to increase the importance of a feature, scale it accordingly. If you scale it by 2, the squares grow by 4. So you have increased the weight.
However, I would just not use k-means for one-hot variables. The mean is for continuous variables, minimizing the sum of squares on a one-hot variable has weird semantics.
answered Apr 16 at 13:34
Anony-MousseAnony-Mousse
5,315625
$endgroup$
add a comment |
$begingroup$
You cannot use k-means clustering algorithm, if your data contains categorical variables and k-modes is suitable for clustering categorigal data. However, there are several algorithms for clustering mixed data, which actually are variationsmodifications of the basic ones.
Please check the following paper:
"Survey of State-of-the-Art Mixed Data Clustering Algorithms", Amir Ahmad and Sheorz Khan, 2019.
answered Apr 16 at 22:18
Christos KaratsalosChristos Karatsalos
54719
$endgroup$
add a comment |
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3 Answers
3
active
oldest
votes
3 Answers
3
active
oldest
votes
active
oldest
votes
active
oldest
votes
$begingroup$
You cannot really use k-means clustering if your data contains categorical variables since k-means uses Euclidian distance which will not make a lot of sense with dummy-variables. Check out the answers to this similar question.
I would suggest, you switch to k-modes for your clustering algorithm. You will find good implementations both for Python and R.
answered Apr 16 at 9:15
georg_ungeorg_un
318111
$endgroup$
add a comment |
$begingroup$
You cannot really use k-means clustering if your data contains categorical variables since k-means uses Euclidian distance which will not make a lot of sense with dummy-variables. Check out the answers to this similar question.
I would suggest, you switch to k-modes for your clustering algorithm. You will find good implementations both for Python and R.
answered Apr 16 at 9:15
georg_ungeorg_un
318111
$endgroup$
add a comment |
$begingroup$
You cannot really use k-means clustering if your data contains categorical variables since k-means uses Euclidian distance which will not make a lot of sense with dummy-variables. Check out the answers to this similar question.
I would suggest, you switch to k-modes for your clustering algorithm. You will find good implementations both for Python and R.
answered Apr 16 at 9:15
georg_ungeorg_un
318111
$endgroup$
You cannot really use k-means clustering if your data contains categorical variables since k-means uses Euclidian distance which will not make a lot of sense with dummy-variables. Check out the answers to this similar question.
I would suggest, you switch to k-modes for your clustering algorithm. You will find good implementations both for Python and R.
answered Apr 16 at 9:15
georg_ungeorg_un
318111
edited Apr 16 at 14:53
answered Apr 16 at 9:15
georg_ungeorg_un
318111
answered Apr 16 at 9:15
georg_ungeorg_un
318111
answered Apr 16 at 9:15
georg_ungeorg_un
318111
318111
add a comment |
add a comment |
$begingroup$
Clearly the objective function uses a sum over the features.
So if you want to increase the importance of a feature, scale it accordingly. If you scale it by 2, the squares grow by 4. So you have increased the weight.
However, I would just not use k-means for one-hot variables. The mean is for continuous variables, minimizing the sum of squares on a one-hot variable has weird semantics.
answered Apr 16 at 13:34
Anony-MousseAnony-Mousse
5,315625
$endgroup$
add a comment |
$begingroup$
Clearly the objective function uses a sum over the features.
So if you want to increase the importance of a feature, scale it accordingly. If you scale it by 2, the squares grow by 4. So you have increased the weight.
However, I would just not use k-means for one-hot variables. The mean is for continuous variables, minimizing the sum of squares on a one-hot variable has weird semantics.
answered Apr 16 at 13:34
Anony-MousseAnony-Mousse
5,315625
$endgroup$
add a comment |
$begingroup$
Clearly the objective function uses a sum over the features.
So if you want to increase the importance of a feature, scale it accordingly. If you scale it by 2, the squares grow by 4. So you have increased the weight.
However, I would just not use k-means for one-hot variables. The mean is for continuous variables, minimizing the sum of squares on a one-hot variable has weird semantics.
answered Apr 16 at 13:34
Anony-MousseAnony-Mousse
5,315625
$endgroup$
Clearly the objective function uses a sum over the features.
So if you want to increase the importance of a feature, scale it accordingly. If you scale it by 2, the squares grow by 4. So you have increased the weight.
However, I would just not use k-means for one-hot variables. The mean is for continuous variables, minimizing the sum of squares on a one-hot variable has weird semantics.
answered Apr 16 at 13:34
Anony-MousseAnony-Mousse
5,315625
answered Apr 16 at 13:34
Anony-MousseAnony-Mousse
5,315625
answered Apr 16 at 13:34
Anony-MousseAnony-Mousse
5,315625
answered Apr 16 at 13:34
Anony-MousseAnony-Mousse
5,315625
5,315625
add a comment |
add a comment |
$begingroup$
You cannot use k-means clustering algorithm, if your data contains categorical variables and k-modes is suitable for clustering categorigal data. However, there are several algorithms for clustering mixed data, which actually are variationsmodifications of the basic ones.
Please check the following paper:
"Survey of State-of-the-Art Mixed Data Clustering Algorithms", Amir Ahmad and Sheorz Khan, 2019.
answered Apr 16 at 22:18
Christos KaratsalosChristos Karatsalos
54719
$endgroup$
add a comment |
$begingroup$
You cannot use k-means clustering algorithm, if your data contains categorical variables and k-modes is suitable for clustering categorigal data. However, there are several algorithms for clustering mixed data, which actually are variationsmodifications of the basic ones.
Please check the following paper:
"Survey of State-of-the-Art Mixed Data Clustering Algorithms", Amir Ahmad and Sheorz Khan, 2019.
answered Apr 16 at 22:18
Christos KaratsalosChristos Karatsalos
54719
$endgroup$
add a comment |
$begingroup$
You cannot use k-means clustering algorithm, if your data contains categorical variables and k-modes is suitable for clustering categorigal data. However, there are several algorithms for clustering mixed data, which actually are variationsmodifications of the basic ones.
Please check the following paper:
"Survey of State-of-the-Art Mixed Data Clustering Algorithms", Amir Ahmad and Sheorz Khan, 2019.
answered Apr 16 at 22:18
Christos KaratsalosChristos Karatsalos
54719
$endgroup$
You cannot use k-means clustering algorithm, if your data contains categorical variables and k-modes is suitable for clustering categorigal data. However, there are several algorithms for clustering mixed data, which actually are variationsmodifications of the basic ones.
Please check the following paper:
"Survey of State-of-the-Art Mixed Data Clustering Algorithms", Amir Ahmad and Sheorz Khan, 2019.
answered Apr 16 at 22:18
Christos KaratsalosChristos Karatsalos
54719
answered Apr 16 at 22:18
Christos KaratsalosChristos Karatsalos
54719
answered Apr 16 at 22:18
Christos KaratsalosChristos Karatsalos
54719
answered Apr 16 at 22:18
Christos KaratsalosChristos Karatsalos
54719
54719
add a comment |
add a comment |
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