Linked Questions

0
votes
1answer
3k views

Does it make sense to run DBSCAN on the output from t-SNE? [duplicate]

Performed time series clustering where I used DTW to generate a distance matrix. The distance matrix was then given as an input to t-SNE where the two-dimensional results from t-SNE were used for ...
86
votes
6answers
68k views

How to tell if data is “clustered” enough for clustering algorithms to produce meaningful results?

How would you know if your (high dimensional) data exhibits enough clustering so that results from kmeans or other clustering algorithm is actually meaningful? For k-means algorithm in particular, ...
38
votes
3answers
19k views

Why is t-SNE not used as a dimensionality reduction technique for clustering or classification?

In a recent assignment, we were told to use PCA on the MNIST digits to reduce the dimensions from 64 (8 x 8 images) to 2. We then had to cluster the digits using a Gaussian Mixture Model. PCA using ...
13
votes
1answer
3k views

What is the good use for t-SNE, apart from data visualization?

In what situations should we use t-SNE (apart from data visualization)? T-SNE is used for dimensionality reduction. The answer to this question suggests that t-SNE should be used only for ...
6
votes
1answer
8k views

How to interpret t-SNE plot?

I want to know how to interpret t-distributed stochastic neighbor embeding (t-SNE) plots. In particular: 1) What information do they convey, besides showing clusters? 2) In PCA we can see loadings and ...
3
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2answers
2k views

Difference between dimensionality reduction and clustering

General practice for clustering is to do some sort of linear/non-linear dimensionality reduction before clustering esp. if the number the number of features are high(say ...
0
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1answer
1k views

DBSCAN considers all data points noise for reduced time series data [closed]

I had a data matrix 609 rows × 264 columns, time-series data. Data was reduced using t-SNE algorithm to 3 dimensions. When being clustered I get zero clusters, where all data points are considered ...
2
votes
1answer
1k views

Word2vec/Doc2vec clustering

Application/Desire : I want to be able to cluster word2vec vectors using density based clustering algorithms (say dbscan/hdbscan; due to too much noise in data) using python or R. I cannot compute ...
1
vote
1answer
538 views

Can t-SNE be directly used as a clustering algorithm?

I've been working on a dataset about a few millions commuters and their travel patterns with around 50 dimensions. And I'm applying t-SNE to the dataset. My initial goal of applying t-SNE was to ...
1
vote
0answers
736 views

Using tsne results to train a model?

I have a dataset that has 5 features; 2 continuous and 3 categorical. If I use one hot encoding on each of the categorical features, I end up with some 600 features for each observation. I then use ...
0
votes
0answers
693 views

Perform normalization before using t-SNE components in any ML algorithms and while appending new features to these components?

I have a large data set (around 4600 rows and 10000 columns). First, I performed L2 normalization and did PCA to obtained 50 components. Then I performed t-SNE and obtained 2 components. I did the ...
1
vote
0answers
494 views

t-SNE dimensionality reduction for classification

Reading about t-SNE, and looking at the pretty plots, it seems to be very good at separating things that we "expect" to be separate in low dimensions. Why wouldn't we use this to do dimensionality ...
1
vote
1answer
51 views

Clustering after t-SNE in R

As explained here, t-SNE maps high dimensional data such as word embedding into a lower dimension in such that the distance between two words roughly describe the similarity. It also begins to create ...