All Questions
29 questions
0
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24
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Curse of dimensionality in Time series with K-means
I have been looking at the following notebook: time series clustering
where the writer says that the dataset is affected by the "Curse of Dimensionality", so applying TimeSeriesKMeans ...
- 1
1
vote
0
answers
68
views
K-means clustering - weird PCA visualization
I performed PCA on 4 variables and are shown in this visualization:
At first look it doesn't look convincing and the some clusters seem weird.
The data was cleaned and standardized beforehand. Only ...
- 11
1
vote
2
answers
384
views
Interpreting results of K-means after PCA
I have this dataset about an airline company customers with 22 explanatory variables. My goal is to perform some sort of customer segmentation with the k-means algorithm. One problem that I've found ...
0
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0
answers
31
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How to interpret the Scatter Plot result from PCA? [duplicate]
I have a project in school about clustering analysis. I have applied standardization and principal component analysis (PCA) to my dataset (I used K-means), which is about heart disease patients. I ...
- 1
2
votes
1
answer
1k
views
Clustering leading to visually overlapping clusters on scatterplot
I am dealing with a dataset with 13 features.
After going through some standard scaling and missing data imputation, I use kmeans from sklearn to create clusters.
Now the point is that, although the ...
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
2
votes
0
answers
158
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How to perform cluster analysis on categorial data in R
I have survey data with 1000 respondents, each one has awnsered 20 questions related to different product features of a car. Each question could be awnsered as "good", "indifferent"...
- 309
1
vote
1
answer
512
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How do I analyze clustering post-PCA
I had in mind to cluster stocks based on some risk indicators such as VaR, sharpe ratio or variance. In a first instance I was thinking to cluster those data points and analyze the results, because ...
1
vote
0
answers
44
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Clustering common words for objects
I am currently running experiments aiming to simulate information transfer between agents. Without going into too much irrelevant detail, following the conclusion of a simulation I am left with a csv ...
- 21
1
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0
answers
19
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K means clusters, would PCA be a better option?
I have the data below. I need to use a clustering method to classify them and into categories of "Heterozygotote, Allele 1, Allele 2 and No Call. The values in RFU1 and RFU2 are used to determine the ...
1
vote
0
answers
532
views
Why does kmeans after SVD result in ideal clusters
I am clustering tweets which are related to eye fashion and they are extracted using keywords like mascara, eyeliner, eyeshadow, etc from twitter.
I
constructed a Tf-idf matrix (tweets x words) ...
- 163
2
votes
2
answers
485
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How to include percentage variables in PCA + K-means when some values are undefined because the denominator is 0?
I'm trying to do customer segmentation by using PCA to reduce dimensionality and then feeding the resulting principal components into a K-means algo to get at the final segments. Some of my variables ...
- 1,554
1
vote
1
answer
585
views
What does it mean to apply k-means algorithm on transformed distance matrix?
I am reading a very good (recent) publication in clustering: Kiselev et al., 2017, SC3 - consensus clustering of single-cell RNA-Seq data (if you don't have access, see author PDF).
The algorithm ...
- 111
5
votes
1
answer
11k
views
How to use Gower's Distance with clustering algorithms in Python
I am trying to cluster by dataset with mixed features using k-means. As a distance metric, I am using Gower's Dissimilarity. I want to ask 2 things:
-Is k-means an appropriate algorithm that can ...
- 151
1
vote
0
answers
8k
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
1
answer
2k
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Is it valid to do K-means clustering on PCA-reduced data?
Here is the basic SKLearn tutorial on K-means.
They run PCA and then do K-means on reduced data. Can it radically affect the result? Will we get totally different clusters if we apply PCA on already ...
- 29
4
votes
4
answers
2k
views
PCA, dimensionality, and k-means results: reaction to duplicating of variables
There are many excellent conversations on CV about the curse of dimensionality when applied to methods like k-means. The answer in the same post and other research (e.g., the paper titled "When Is ‘...
- 520
0
votes
1
answer
6k
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PCA vs tSNE after k-means clustering: some separation on the PCA plot but no separation on the tSNE plot
I am using K-Means in order to cluster a population based on 5 variables into 2 groups. I am then using both tSNE and PCA to visualise the outcome to somehow better understand the separation.
What is ...
- 201
1
vote
1
answer
324
views
Is it possible to use PCA scores and some additional individual variables for cluster analysis?
I am trying to conduct the k-means cluster analysis.
Because some variables are highly related, I conducted PCA to reduce variables.
But some variable shows low communalities and the KMO is lower than ...
- 11
2
votes
0
answers
420
views
A problem with implementing PCA-guided k-means
I am new to machine learning. I am reading the papers K-means Clustering via Principal Component Analysis and PCA-guided search for K-means. But there are too many mathematical proofs in these papers. ...
- 21
2
votes
1
answer
662
views
Is it problematic to include negatively correlated variables in a K-Means estimator?
I'm running a simple K-Means clustering algorithm on a dataset with ~15 features. Some of the features I'm currently including in my model represent different percentages of the same whole. I'd prefer ...
0
votes
1
answer
116
views
Using original centroid as cluster identifier after applying PCA
Take a look at my original data. (masked with purely random alphabetic here) :
a b c d e
f g h i j
A = k l m n o
p q r s t
u v w x y
I'm running ...
- 111
118
votes
6
answers
176k
views
What is the relation between k-means clustering and PCA?
It is a common practice to apply PCA (principal component analysis) before a clustering algorithm (such as k-means). It is believed that it improves the clustering results in practice (noise reduction)...
- 4,458
4
votes
1
answer
1k
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Is there anything wrong with performing EM clustering on PCA output?
I am trying to separate my dataset into meaningful clusters. I have tried k-means, hierarchical and EM clustering (fitting a gaussian mixture model using EM algorithm, using the EMCluster R package) ...
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39
votes
2
answers
57k
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How would PCA help with a k-means clustering analysis?
Background: I want to classify the residential areas of a city into groups based on their social-economic characteristics, including housing unit density, population density, green space area, housing ...
- 605
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
4
votes
2
answers
194
views
Controling segmentation process in order to get usable segments
My aim is to create segments based on survey data. This in it self is quite straight forward: I use PCA to extract information from the survey answers, and then ...
- 1,182
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
1
vote
1
answer
1k
views
Can component scores be used for further analyses, e.g. cluster analysis?
I have done a principal component analysis using SPSS and now have 3 components.
2 components have 4 items in the subscale, and 1 component has 3 items. Component scores using regression for each ...
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