All Questions
11 questions
1
vote
1
answer
300
views
Applying k-means over PCA
I have a dataset containing 20 columns and 200 rows. This is an unlabeled dataset and I applied PCA to this dataset for dimensionality reduction. After successfully using PCA, I received a dataset ...
- 19
1
vote
0
answers
176
views
Unsupervised methods for a dataset that contains ordinal categorical variables casted as numerical
I'm fairly new to statistical learning. At the moment, I'm using the EU-SILC dataset to analyse the factors that determine the French income (x). It can be found here: https://ec.europa.eu/eurostat/...
- 135
1
vote
0
answers
201
views
How to plot categories after clustering
I am trying to plot the categories I have obtained via DBSCAN on a 30-dimensional dataset 12 categories, and I want to visualize them in a 2d plot.
My procedure was to reduce that 30-dimensional ...
- 11
1
vote
1
answer
3k
views
Double zeroes problem with euclidean distance and abundance data - is the problem widely varying abundances or mutually missing taxa?
Background:
I have data from a study of trailside and forest interior transects, where half my transects are on-trail and half in the forest.
The data is in the form of raw species abundances, ...
- 111
2
votes
1
answer
1k
views
How does Principal Component Analysis help me understand my data?
I have a dataset which contains 10000 examples. Each example has 100 dimensions. These dimensions have the same scale.
I clustered all examples using their 100-dimensional vectors and drew the elbow ...
- 2,095
11
votes
2
answers
7k
views
Difference between PCA and spectral clustering for a small sample set of Boolean features
I have a dataset of 50 samples. Each sample is composed of 11 (possibly correlated) Boolean features. I would like to some how visualize these samples on a 2D plot and examine if there are clusters/...
- 271
9
votes
1
answer
13k
views
Understanding cluster plot and component variability
I have run k-means clustering. I have also plotted the results using the following code in R:
...
- 389
8
votes
1
answer
7k
views
Interpret clustering plotted in the first two principal components
I got this plot when I plotted a clustering object in R. If I run km <- clara(data, 2), then plot(km), I get a similar plot. ...
- 595
3
votes
1
answer
8k
views
Interpretation of PCA biplot?
I just ran my first ever PCA, so please excuse any naivety on my part.
As input, I used five years worth of the following:
S&P/ASX 200 A-REIT
S&P/ASX 200 Consumer Discretionary
S&P/ASX ...
- 41
7
votes
3
answers
2k
views
Appropriateness of PCA to visualize clusters in genetic data
I've seen PCA improperly applied in genetic research quite often. I wanted to clarify : when is it appropriate to use PCA as a visualization tool in your analysis?
Some examples:
1) Rarely is the % ...
- 1,661
9
votes
5
answers
3k
views
Dimensionality reduction technique to maximize separation of known clusters?
So let's say I have a bunch of data points in R^n, where n is pretty big (like, 50). I know this data falls into 3 clusters, and I know which cluster each data point is a part of. All I want to do is ...
- 143