How to give a higher importance to certain features in a (k-means) clustering model?K-Means clustering for mixed numeric and categorical dataperform cluster on a multiple dimensional data in RCalculate 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?Perform k-means clustering over multiple columnsClustering, Mixed Data Set with Ordinal and Nominal Scale Data
Can an NPC use the teleport spell to affect an object they can see with the scry spell?
Can 35 mm film which went through a washing machine still be developed?
Can I voluntarily exit from the US after a 20 year overstay, or could I be detained at the airport?
Can I return my ability to cast Wish by using Glyph of warding?
As an interviewer, how to conduct interviews with candidates you already know will be rejected?
C - Learning Linked Lists, Pointer Manipulation - Store some ints, print and free memory
Does Hogwarts have its own anthem?
Did Joe Biden "stop a prosecution" into his son in Ukraine? And did he brag about stopping the prosecution?
Would houseruling two or more instances of resistance to the same element as immunity be overly unbalanced?
How fast are we moving relative to the CMB?
In search of a pedagogically simple example of asymmetric encryption routine?
Find all matrices satisfy
Sci-fi story about aliens with cells based on arsenic or nitrogen, poisoned by oxygen
Determining if auto stats update is in progress
What's the correct way to determine turn order in this situation?
Redirect output on-the-fly - looks not possible in Linux, why?
Check reference list in pandas column using numpy vectorization
As a girl, how can I voice male characters effectively?
What makes a character irredeemable?
Is it unethical to give a gift to my professor who might potentially write me a LOR?
How to explain that the sums of numerators over sums of denominators isn't the same as the mean of ratios?
Mac no longer boots
Sum of series with addition
Enumerating all permutations that are "square roots" of derangements
How to give a higher importance to certain features in a (k-means) clustering model?
K-Means clustering for mixed numeric and categorical dataperform cluster on a multiple dimensional data in RCalculate 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?Perform k-means clustering over multiple columnsClustering, Mixed Data Set with Ordinal and Nominal Scale Data
.everyoneloves__top-leaderboard:empty,.everyoneloves__mid-leaderboard:empty,.everyoneloves__bot-mid-leaderboard:empty
margin-bottom:0;
$begingroup$
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 bronze badges
$endgroup$
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 bronze badges
$endgroup$
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 bronze badges
$endgroup$
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 bronze badges
asked Apr 16 at 8:33
EvaEva
363 bronze badges
asked Apr 16 at 8:33
EvaEva
363 bronze badges
asked Apr 16 at 8:33
EvaEva
363 bronze badges
asked Apr 16 at 8:33
EvaEva
363 bronze badges
363 bronze badges
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 categorical variables. Check out the answers to this similar question.
You can use the following rules for performing clustering with k-means or one of its derivates:
If your data contains only metric variables:
Scale the data and use k-means (R) (Python).
If your data contains only categorical variables:
Use k-modes (R) (Python).
If your data contains categorical and metric variables:
Scale the metric variables and use k-prototypes (R) (Python).
answered Apr 16 at 9:15
georg-ungeorg-un
8282 silver badges17 bronze badges
$endgroup$
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
6,1198 silver badges28 bronze badges
$endgroup$
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
6522 silver badges10 bronze badges
$endgroup$
add a comment
|
Your Answer
StackExchange.ready(function()
var channelOptions =
tags: "".split(" "),
id: "557"
;
initTagRenderer("".split(" "), "".split(" "), channelOptions);
StackExchange.using("externalEditor", function()
// Have to fire editor after snippets, if snippets enabled
if (StackExchange.settings.snippets.snippetsEnabled)
StackExchange.using("snippets", function()
createEditor();
);
else
createEditor();
);
function createEditor()
StackExchange.prepareEditor(
heartbeatType: 'answer',
autoActivateHeartbeat: false,
convertImagesToLinks: false,
noModals: true,
showLowRepImageUploadWarning: true,
reputationToPostImages: null,
bindNavPrevention: true,
postfix: "",
imageUploader:
brandingHtml: "Powered by u003ca class="icon-imgur-white" href="https://imgur.com/"u003eu003c/au003e",
contentPolicyHtml: "User contributions licensed under u003ca href="https://creativecommons.org/licenses/by-sa/4.0/"u003ecc by-sa 4.0 with attribution requiredu003c/au003e u003ca href="https://stackoverflow.com/legal/content-policy"u003e(content policy)u003c/au003e",
allowUrls: true
,
onDemand: true,
discardSelector: ".discard-answer"
,immediatelyShowMarkdownHelp:true
);
);
Sign up or log in
StackExchange.ready(function ()
StackExchange.helpers.onClickDraftSave('#login-link');
);
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
StackExchange.ready(
function ()
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fdatascience.stackexchange.com%2fquestions%2f49381%2fhow-to-give-a-higher-importance-to-certain-features-in-a-k-means-clustering-mo%23new-answer', 'question_page');
);
Post as a guest
Required, but never shown
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 categorical variables. Check out the answers to this similar question.
You can use the following rules for performing clustering with k-means or one of its derivates:
If your data contains only metric variables:
Scale the data and use k-means (R) (Python).
If your data contains only categorical variables:
Use k-modes (R) (Python).
If your data contains categorical and metric variables:
Scale the metric variables and use k-prototypes (R) (Python).
answered Apr 16 at 9:15
georg-ungeorg-un
8282 silver badges17 bronze badges
$endgroup$
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 categorical variables. Check out the answers to this similar question.
You can use the following rules for performing clustering with k-means or one of its derivates:
If your data contains only metric variables:
Scale the data and use k-means (R) (Python).
If your data contains only categorical variables:
Use k-modes (R) (Python).
If your data contains categorical and metric variables:
Scale the metric variables and use k-prototypes (R) (Python).
answered Apr 16 at 9:15
georg-ungeorg-un
8282 silver badges17 bronze badges
$endgroup$
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 categorical variables. Check out the answers to this similar question.
You can use the following rules for performing clustering with k-means or one of its derivates:
If your data contains only metric variables:
Scale the data and use k-means (R) (Python).
If your data contains only categorical variables:
Use k-modes (R) (Python).
If your data contains categorical and metric variables:
Scale the metric variables and use k-prototypes (R) (Python).
answered Apr 16 at 9:15
georg-ungeorg-un
8282 silver badges17 bronze badges
$endgroup$
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 categorical variables. Check out the answers to this similar question.
You can use the following rules for performing clustering with k-means or one of its derivates:
If your data contains only metric variables:
Scale the data and use k-means (R) (Python).
If your data contains only categorical variables:
Use k-modes (R) (Python).
If your data contains categorical and metric variables:
Scale the metric variables and use k-prototypes (R) (Python).
answered Apr 16 at 9:15
georg-ungeorg-un
8282 silver badges17 bronze badges
edited Aug 9 at 13:05
answered Apr 16 at 9:15
georg-ungeorg-un
8282 silver badges17 bronze badges
answered Apr 16 at 9:15
georg-ungeorg-un
8282 silver badges17 bronze badges
answered Apr 16 at 9:15
georg-ungeorg-un
8282 silver badges17 bronze badges
8282 silver badges17 bronze badges
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
6,1198 silver badges28 bronze badges
$endgroup$
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
6,1198 silver badges28 bronze badges
$endgroup$
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
6,1198 silver badges28 bronze badges
$endgroup$
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
6,1198 silver badges28 bronze badges
answered Apr 16 at 13:34
Anony-MousseAnony-Mousse
6,1198 silver badges28 bronze badges
answered Apr 16 at 13:34
Anony-MousseAnony-Mousse
6,1198 silver badges28 bronze badges
answered Apr 16 at 13:34
Anony-MousseAnony-Mousse
6,1198 silver badges28 bronze badges
6,1198 silver badges28 bronze badges
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
6522 silver badges10 bronze badges
$endgroup$
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
6522 silver badges10 bronze badges
$endgroup$
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
6522 silver badges10 bronze badges
$endgroup$
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
6522 silver badges10 bronze badges
answered Apr 16 at 22:18
Christos KaratsalosChristos Karatsalos
6522 silver badges10 bronze badges
answered Apr 16 at 22:18
Christos KaratsalosChristos Karatsalos
6522 silver badges10 bronze badges
answered Apr 16 at 22:18
Christos KaratsalosChristos Karatsalos
6522 silver badges10 bronze badges
6522 silver badges10 bronze badges
add a comment
|
add a comment
|
Thanks for contributing an answer to Data Science Stack Exchange!
- Please be sure to answer the question. Provide details and share your research!
But avoid …
- Asking for help, clarification, or responding to other answers.
- Making statements based on opinion; back them up with references or personal experience.
Use MathJax to format equations. MathJax reference.
To learn more, see our tips on writing great answers.
Sign up or log in
StackExchange.ready(function ()
StackExchange.helpers.onClickDraftSave('#login-link');
);
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
StackExchange.ready(
function ()
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fdatascience.stackexchange.com%2fquestions%2f49381%2fhow-to-give-a-higher-importance-to-certain-features-in-a-k-means-clustering-mo%23new-answer', 'question_page');
);
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function ()
StackExchange.helpers.onClickDraftSave('#login-link');
);
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function ()
StackExchange.helpers.onClickDraftSave('#login-link');
);
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function ()
StackExchange.helpers.onClickDraftSave('#login-link');
);
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
