Linked Questions
13 questions linked to/from How do I know my k-means clustering algorithm is suffering from the curse of dimensionality?
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K-means with high dimensional data [duplicate]
I read in many places that k-means clustering algorithm does not perform well when dealing with multidimensional binary data (so vectors whose entries are zero or one).
Intuitively, it is pretty ...
445
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5
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How to understand the drawbacks of K-means
K-means is a widely used method in cluster analysis. In my understanding, this method does NOT require ANY assumptions, i.e., give me a dataset and a pre-specified number of clusters, k, and I just ...
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Why is Euclidean distance not a good metric in high dimensions?
I read that 'Euclidean distance is not a good distance in high dimensions'. I guess this statement has something to do with the curse of dimensionality, but what exactly? Besides, what is 'high ...
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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,...
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Examples of PCA where PCs with low variance are "useful"
Normally in principal component analysis (PCA) the first few PCs are used and the low variance PCs are dropped, as they do not explain much of the variation in the data.
However, are there examples ...
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How to use both binary and continuous variables together in clustering?
I need to use binary variables (values 0 & 1) in k-means. But k-means only works with continuous variables. I know some people still use these binary variables in k-means ignoring the fact that k-...
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Does "curse of dimensionality" really exist in real data?
I understand what is "curse of dimensionality", and I have done some high dimensional optimization problems and know the challenge of the exponential possibilities.
However, I doubt if the "curse of ...
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What is the purpose of row normalization
I understand the reasoning behind column normalization, as it causes features to be weighted equally, even if they are not measured on the same scale - however, often in the nearest neighbour ...
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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 ‘...
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1
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1
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Intrinsic dimensionality and density-based clustering
I’ve got several thousand observations in 350-dimensional space, in a relatively sparse matrix (median observation has 11 non-zero dimensions). I'm using a density-based clustering algorithm, DBSCAN, ...
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K means clustering breakup---galaxy spectrum data set
I have a spectrum data set (total 22000). Similar to an electronic wave data, two dimensional (Flux vs Wavelength). A typical set of wavelength plot looks like below
Now I am doing kmeans on this ...
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Unsupervised learning: How to identify differences between clusters?
I'm learning about unsupervised learning and I tried to use KMeans, AgglomerativeClustering and DBSCAN on the same datase. The result was ok, they seems to work fine according silhouette_score() ...
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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 ...
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