Linked Questions

1 vote
0 answers

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 ...
Bruce Jinru Su's user avatar
2 votes
1 answer

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 ...
lhay86's user avatar
  • 143
1 vote
0 answers

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. ...
Dory's user avatar
  • 11
0 votes
0 answers

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: ...
melatonin15's user avatar
0 votes
0 answers

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?
sherek_66's user avatar
  • 137
40 votes
1 answer

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, ...
user116948's user avatar
19 votes
8 answers

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'...
Giorgio Spedicato's user avatar
10 votes
3 answers

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 ...
mickkk's user avatar
  • 929
22 votes
3 answers

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....
lockedoff's user avatar
  • 1,995
5 votes
2 answers

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 ...
Michael's user avatar
  • 51
5 votes
1 answer

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,...
brett's user avatar
  • 53
4 votes
1 answer

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 ...
user2991243's user avatar
  • 3,951
5 votes
1 answer

PCA or MCA for binarized data

I am working with bioinformatics and I have data that looks like the following: ...
masfenix's user avatar
  • 481
-1 votes
2 answers

Principle Component Analysis on categorical predictors [duplicate]

I tried running prcomp() on my training set, which contains some categorical/factor predictors (as well as a binary response), and was given an error saying my data needs to be numeric. Can PCA not be ...
SugarMarsh's user avatar
3 votes
1 answer

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 ...
user113791's user avatar

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