Linked Questions
38 questions linked to/from Why does k-means clustering algorithm use only Euclidean distance metric?
16
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1
answer
42k
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Cosine Distance as Similarity Measure in KMeans [duplicate]
I am currently solving a problem where I have to use Cosine distance as the similarity measure for k-means clustering. However, the standard k-means clustering package (from Sklearn package) uses ...
- 275
6
votes
3
answers
10k
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Is there a situation when one would use L1 norm over L2 norm in k-means algorithm? [duplicate]
Is there a situation when one would use L1 norm over L2 norm in k-means algorithm?
In most of the articles online, k-means all deal with l2-norm. L1 norm does not seem to be useful because it is not ...
2
votes
1
answer
8k
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Why is it bad to use Pearson distance in K-means clustering? [duplicate]
I have implemented this algorithm in MATLAB and when I produce plots I notice that using Euclidean distance, I usually get presented with a clear pattern (sum of squares decreases with the number of ...
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2
votes
0
answers
877
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Is pairwise distance matrix useful to k-means? [duplicate]
The k-means implemented in scikit-learn precomputes distances but I don't how these distances are used. In its standard version, k-means is known to compute only the distances between the points and ...
1
vote
1
answer
352
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is k-means generalizable at any distance? [duplicate]
The classical version of k-means uses the Euclidean distance in the first step, and the arithmetic mean (the value center) in the second step. Is k-means generalizable to other distances and other ...
31
votes
8
answers
36k
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Perform K-means (or its close kin) clustering with only a distance matrix, not points-by-features data
I want to perform K-means clustering on objects I have, but the objects aren't described as points in space, i.e. by objects x features dataset. However, I am able ...
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63
votes
3
answers
45k
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How to select a clustering method? How to validate a cluster solution (to warrant the method choice)?
One of the biggest issue with cluster analysis is that we may happen to have to derive different conclusion when base on different clustering methods used (including different linkage methods in ...
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55
votes
2
answers
44k
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Hierarchical clustering with mixed type data - what distance/similarity to use?
In my dataset we have both continuous and naturally discrete variables. I want to know whether we can do hierarchical clustering using both type of variables. And if yes, what distance measure is ...
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35
votes
4
answers
50k
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Clustering a correlation matrix
I have a correlation matrix which states how every item is correlated to the other item. Hence for a N items, I already have a N*N correlation matrix. Using this correlation matrix how do I cluster ...
- 483
21
votes
4
answers
25k
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How to understand the drawbacks of Hierarchical Clustering?
Can someone explain the pros and cons of Hierarchical Clustering?
Does Hierarchical Clustering have the same drawbacks as K means?
What are the advantages of Hierarchical Clustering over K means?
...
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22
votes
4
answers
39k
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k-means implementation with custom distance matrix in input
Can anyone point me out a k-means implementation (it would be better if in matlab) that can take the distance matrix in input?
The standard matlab implementation needs the observation matrix in input ...
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30
votes
1
answer
25k
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Converting similarity matrix to (euclidean) distance matrix
In Random forest algorithm, Breiman (author) constructs similarity matrix as follows:
Send all learning examples down each tree in the forest
If two examples land in the same leaf increment ...
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11
votes
3
answers
25k
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K-means on cosine similarities vs. Euclidean distance (LSA)
I am using latent semantic analysis to represent a corpus of documents in lower dimensional space. I want to cluster these documents into two groups using k-means.
Several years ago, I did this using ...
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10
votes
2
answers
27k
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K-means: Why minimizing WCSS is maximizing Distance between clusters?
From a conceptual and algorithmic standpoint, I understand how K-means works. However, from a mathematical standpoint, I don't understand why minimizing the WCSS (within-cluster sums of squares) will ...
- 504
11
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3
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9k
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Using k-means with other metrics
So I realize this has been asked before: e.g. What are the use cases related to cluster analysis of different distance metrics? but I've found the answers somewhat contradictory to what is suggested ...
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