Linked Questions

5
votes
2answers
1k views

Need more intuition for the curse of dimensionality [duplicate]

People despise using Eucliean distance in higher dimensional spaces because it is not a viable metric. People argue that the distance between two vectors becomes very large as the number of dimensions ...
0
votes
0answers
13 views

Is curse of dimensionality more harmful for distance based models? [duplicate]

I want to understand what exactly is curse of dimensionality and how does it effect the model performance. Does the the concept apply to all the models?. Is it equally bad for distance based models ...
99
votes
11answers
34k views

Explain “Curse of dimensionality” to a child

I heard many times about curse of dimensionality, but somehow I'm still unable to grasp the idea, it's all foggy. Can anyone explain this in the most intuitive way, as you would explain it to a child,...
83
votes
7answers
30k views

Euclidean distance is usually not good for sparse data (and more general case)?

I have seen somewhere that classical distances (like Euclidean distance) become weakly discriminant when we have multidimensional and sparse data. Why? Do you have an example of two sparse data ...
12
votes
2answers
10k views

How do I know my k-means clustering algorithm is suffering from the curse of dimensionality?

I believe that the title of this question says it all.
8
votes
1answer
10k views

Why is dimensionality reduction always done before clustering? [duplicate]

I learned that it's common to do dimensionality reduction before clustering. But, is there any situation that it is better to do clustering first, and then do dimensionality reduction?
8
votes
1answer
940 views

Where are most points in a uniformly distributed high-dimensional ball?

Should they be close to the middle (origin) or close its surface?
1
vote
3answers
4k views

Is K-means performance a bottleneck everywhere?

I've read a paper about a sped-up version of k-means: Ding et al. (2015). Yinyang K-Means: A Drop-In Replacement of the Classic K-Means with Consistent Speedup. Now I wonder, is k-means' ...
4
votes
1answer
5k views

What is high dimensional data in data mining?

Currently I am studying effect of high dimensions of data on clustering , for experiment purpose I want to use kdd dataset from UCI which contains 42 features. Is kdd a high dimensional data or what ...
2
votes
3answers
6k views

Distance measure methods of R function dist() evaluation

I want to compute the distance matrix for the columns on a 1000 x 230 matrix using the dist() function in R. Though, I am uncertain about which method to use. I ...
5
votes
1answer
1k views

Why is the curse of dimensionality also called the empty space phenomenon?

The curse of dimensionality refers to the fact that the huge number of correlated features tends to increase the complexity of the treatment that has to be applied to the data set. This is also called ...
7
votes
1answer
2k views

A valid distance metric for high dimensional data

I asked a question about forming a valid distance metric yesterday (Link1) and got some very good answer; however, I have got some more questions about forming a proper distance metric for high ...
1
vote
3answers
2k views

Standardizing some features in K-Means

I have 21 features in my dataset, some features are more important than others. As a fact I know, if I don't standardize (mean=0, SD=1) any features, then features with low variance will have slightly ...
1
vote
2answers
1k views

SOM based on a not euclidean distance

Suppose one has trained a SOM on a certain number of data. Without explaining all the procedure, one can say that the SOM algorithm produces a certain number of prototypes and the new elements coming ...
1
vote
1answer
889 views

How to choose an internal clustering evaluation metric?

I am trying to cluster textual data using fastText vectors with different clustering algorithms, mainly K-Means and DBSCAN. I would like to know which internal evaluation metric works best with K-...

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