Linked Questions

0
votes
0answers
5k views

PCA on categorical variables [duplicate]

I am working on a dataset with many categorical variables for a clustering problem. I've done one-hot encoding where a categorical column with 5 levels will become 5 columns, each has the standard ...
2
votes
1answer
4k views

PCA with continuous and categorical features [duplicate]

I have a dataset with both continuous and categorical features. I want to reduce the dimensionality, but cannot apply PCA directly on the dataset because of the categorical features. One solution I ...
1
vote
0answers
1k views

Categorical Principal Component Analysis [duplicate]

I am planning to perform a Categorical Principal Component Analysis, I have 14 variables with categorical ordinal data (from a 5 point likert scale) and one variable with categorical nominal data. ...
0
votes
0answers
361 views

PCA on Wine data with only one binary data(white/red wine) and other quantitative data [duplicate]

I am working on wine data with the following format: ...
0
votes
0answers
39 views

How I can work with data set contains both numerical and non-numerical features? [duplicate]

I have a dataset contains both numerical and non-numerical columns. I want to use PCA, is there any way to handle both?
0
votes
0answers
21 views

How do I apply PCA to a dataet containing continuous and categorical features? [duplicate]

I wanted to know if we could apply to the dummy varaible columns of the dataset too. As it is binary idk if that should be applied. Please provide a good source to learn PCA if any of you have it, ...
19
votes
8answers
49k views

Clustering of mixed type data with R

I wonder whether it is possible to perform within R a clustering of data having mixed data variables. In other words I have a data set containing both numerical and categorical variables within and I'...
33
votes
1answer
28k views

Is there Factor analysis or PCA for ordinal or binary data?

I have completed the principal component analysis (PCA), exploratory factor analysis (EFA), and confirmatory factor analysis (CFA), treating data with likert scale (5-level responses: none, a little, ...
9
votes
3answers
2k views

How can I tell that there is no pattern in the PCA results?

I have a 1000+ samples dataset of 19 variables. My objective is to predict a binary variable based on the other 18 variables (binary and continuous). I'm quite confident that 6 of the predicting ...
22
votes
3answers
1k views

First step for big data ($N = 10^{10}$, $p = 2000$)

Suppose you are analyzing a huge data set at the tune of billions of observations per day, where each observation has a couple thousand sparse and possibly redundant numerical and categorial variables....
5
votes
2answers
6k views

How to handle both text and numbers for PCA in R?

I'm relatively new to R and am working with a very large dataset that has a mix of numerical scores (for instance, household income) as well as text values (i.e. race). I was planning on using PCA to ...
4
votes
1answer
2k views

Is continuous inputs an assumption of factor analysis?

Should we use only continuous inputs for factor analysis (FA)? My data is a mix of continuous and categorical inputs: one of the inputs has only 600, 700 and 1000 as values. I found that principal ...
3
votes
1answer
3k views

PCA or MCA for binarized data

I am working with bioinformatics and I have data that looks like the following: ...
5
votes
1answer
736 views

Combining together principal components from PCA performed on different subsets of a large dataset

I'm trying to QA a process in which the data has over a million rows with approx 60,000 variables in a binary form. The aim of the process was to perform k-means clustering, but prior to this, the 60,...
3
votes
1answer
2k views

Why convert categorical data into numerical using one hot encoding

I don't have very strong statistical background, and I'm new in data science... Now, I am practicing PCA (Principle Component Analysis) for dimension reduction. This tutorial looks very complete, but ...

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