All Questions
Tagged with k-means dimensionality-reduction
15 questions
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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 ...
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1
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153
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Applying clustering algorithms after t-SNE in R
So I'm doing my bachelor`s work and I'm applying different clustering algorithms on certain data. Before all the clustering of course I'm using a dimensionality reduction algorithm such as t-SNE for ...
Danielius Lesun
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140
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In $k$-means, how is it NP-hard if the dimensionality of the data is at least $2$ ($d\geq 2$)?
In $k$-means, how is it NP-hard if the dimensionality of the data is at least $2$ ($d\geq 2$)? Can someone justify or give reasons to this statement?
Any guidance would be appreciated.
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How would one use Voronoi diagrams for KMeans for high dimensional data?
I am reading Aurelien Geron's Hands on Machine Learning, and in the Unsupervised learning chapter he demonstrates how to create a Voronoi diagram after performing K-Means clustering, and produces the ...
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1
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108
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Correct clustering approach for segmenting stores
Domain : Retail
I have a set of stores which I want to cluster into similar stores based on 10 variables: revenue, avg income, market share etc. I took 2 approach:
Approach 1: Given there are 10 ...
- 163
1
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2
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671
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How to plot High Dimensional supervised K-means on a 2D plot chart
I'm Having a ML problem where my data set contains 80 features labelled into 3 groups (0, 1, -1).
I want to plot the data on a 2D surface to see how "close" (similar) data with ...
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273
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K-means: why reduce dimensions first? [duplicate]
I'm a bit confused about the usefulness of reducing dimensions before doing a k-means clustering.
Suppose you want to apply k-means to a set points $(x_i)$ with high dimension. You want to minimize ...
- 8,757
12
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3
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Clustering as dimensionality reduction
I'm reading a book "Machine learning with Spark" by Nick Pentreath, and at page 224-225 the author discusses about using K-means as a form of dimensionality reduction.
I have never seen this kind of ...
- 1,260
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2
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What should be the optimum number of features for 10 million observations for kmeans clustering?
I have a dataset of 10 million observations and 100 million features. I have to perform kmeans clustering on that dataset. The approx value of k is 30000
Is it advisable to perform clustering with ...
- 11
2
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2
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900
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Cluster centroids vs PCA as a preprocessing stage
What are the pros and cons of using K-means centroids (using cluster centroids as samples) vs Principal Component Analysis (PCA) as a preprocessing stage in machine learning? In what situation is ...
- 1,179
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Understanding this PCA plot of ice cream sales vs temperature
I'm taking a dummy data of temperature vs Ice cream Sales and categorized it using K Means (n clusters = 2) to distinguish 2 categories (totally dummy).
Now I'm doing a Principal Component Analysis ...
- 569
2
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1
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662
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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 ...
39
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2
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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
1
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1
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997
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Noise in clustering of high dimensional sparse data
Questions:
1) How to detect noise variables in high dimensional data?
2) Does the method that is presented below make sense?
3) What clustering methods are most insensitive to random variables in data?...
- 4,034
2
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2
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1k
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Evaluation of k-means output for >3D
I'm implementing the k-means algorithm (in R Map-Reduce) and I wanted to verify if the output I'm getting is close enough to the true centroids of the cluster. This is how I'm verifying with a 2D ...
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