# Questions tagged [clustering]

Clustering is grouping (partitioning) a set of objects so that items in the same group are more similar to each other than to items in different groups, where the notion of similarity may be variously defined.

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### I'm doing clustering using k-maps but in the end all the vales come in the same cluster

This is the data im working with Using cases 6,9,15 as the initial cluster centers Second Using the mean values of the clusters insted of the initial values. There some change in the clustering. ...
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### Plot data points according to the pairwise distance matrix

Consider eight data points. The following matrix (i.e., a symmetric matrix with the lower triangle elements) shows the pairwise distances between any two points. 0 11 0 5 13 0 12 2 14 0 7 17 1 18 0 ...
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### How to determine if a positive integer is comparatively small

Given a set of positive integers, what is a basic statistical method that cuts off the smallest numbers in the set if the method (fed some parameter) determines they are "negligible" or &...
15 views

### Hierarchical Clustering with Ward Distance

I know how hierarchical clustering (with a certain definition of inter-cluster distance) works. And I know that Ward's procedure is based on the goal of minimizing the sum of the squared errors ...
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### Compute Gower's distance manually

Given a=(1,0,13,apple) b=(1, 1, NA, pear) c=(0,1,12,apple) The first two elements for each ...
33 views

### Any common methods to integrate $\frac {\int dx P(x) x r(x)}{\int dx P(x) r(x)}$ when $P(x)$ is a guassian distribution?

I am currently studying David MacKay's Information Theory, Inference, and Learning Algorithms. In chapter 20, where he talks about modify kmean algorithm into a soft kmean algorithm (Gaussian Mixture ...
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### Optimal k-means clustering solution for 2x2 and 3x3 blocks of quadratic grids (proof or proof idea sought)

Condider a set $\mathcal{X}$ of $n \times n$ data points arranged on a quadratic grid which should be quantized by a single centroid (i.e., $k$-means clustering with $k=1$). The optimal position for ...
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### Reducing features from k-means and hierarchical clusters

I have a dataset with 170 features. I use K-means and hierarchical clustering to determine the "optimal" number of clusters (between 10-12). I calculate clustered permutation feature ...
14 views