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Can you use PCA on categorical variables?
While it is technically possible to use PCA on discrete variables, or categorical variables that have been one hot encoded variables, you should not. Simply put, if your variables don’t belong on a coordinate plane, then do not apply PCA to them.
Can we use K-means for categorical variables in K-means?
The k-Means algorithm is not applicable to categorical data, as categorical variables are discrete and do not have any natural origin. So computing euclidean distance for such as space is not meaningful.
How do you use K mean categorical data clustering?
How does the KModes algorithm work?
- Pick K observations at random and use them as leaders/clusters.
- Calculate the dissimilarities and assign each observation to its closest cluster.
- Define new modes for the clusters.
- Repeat 2–3 steps until there are is no re-assignment required.
How to generalize PCA to categorical and numerical data?
On the other hand, the square correlation ratio 𝜂² (A,Y) between a categorical variable A and a numerical variable Y also takes values between 0 and 1. An intuitive way to interpret this metric is the following: the more 𝜂 ² (A,Y) is close to 1, the more the knowledge of the modality taken by A gives a powerful indication on the value taken by Y.
How to calculate PCA for categorical variables in Python?
The following publication shows great and meaningful results when computing PCA on categorical variables treated as simplex vertices: Niitsuma H., Okada T. (2005) Covariance and PCA for Categorical Variables. In: Ho T.B., Cheung D., Liu H. (eds) Advances in Knowledge Discovery and Data Mining.
Which is an example of PCA for categorical features?
PCA is a dimensionality reduction method that can be applied any set of features. Here is an example using OneHotEncoded (i.e. categorical) data: I think pca is reducing var by leverage the linear relation between vars.
Is it a problem if PCA assumes equal weight?
My concern is that PCA assumes equal weight among all features and the original categorical variable (now in 5 columns) will have 5 times the weight than a normal numerical variable. Is it a problem? One thing to do, I think, is to divide the values in the 5 columns by the square root of 5, then the sum of 5 columns’ variance is back to 1.