# Questions tagged [k-means]

k-means is a method to partition data into clusters by finding a specified number of means, k, s.t. when data are assigned to clusters w/ the nearest mean, the w/i cluster sum of squares is minimized

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### Initializing EM algortihm with kmean when means are the same

I have a set of point (in one dimension) of 2 types: -First type of point: generated with gaussian density with parameters (m1,sigma1) -Second type of point: generated with gaussian density with ...
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### Why is it wrong to apply k-means to a distance matrix?

There are several threads discussing clustering analysis of a distance matrix and they dismiss use of the k-means algorithm. Here are two examples: Perform K-means (or its close kin) clustering with ...
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### How to compare clustering results between “raw” and normalized data

I have a dataset and I would like to apply a clustering algorithm to find some groups. I do not have any label, so it is just wondering if I can find relevant clusters. If it may help, it is ...
1answer
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### K-Means clustering - upper bound for number of iterations

Suppose we run the K-means clustering algorithm on a one-dimensional dataset, i.e. $p = 1$, so that each observation consists of a single real number. We assume that these real numbers are distinct. ...
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### KMeans where there are clusters of data with different densities

Suppose we have $n$ data samples and they are grouped in two sets such that half of the data are in a high density region and the other half is in a low density region. These regions are apart from ...
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### Automation of k-means for customer clustering

Im working on the project to cluster customers each month and the final idea is to create auto process (script) which will run the k-means so i can say - Cluster 0 - Loyals Cluster 1 - Active Cluster ...
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### Discussing validity of tests performed after a cluster analysis

I'm new to datascience (from a medical/medical science background). My supervisor (social sciences background) asked me to assist in rewriting a paper where we do a cluster analysis for a ...
2answers
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### Why is the clustering cost function called “distortion”?

Andrew Ng's excellent ML course on Coursera describes the k-means clustering algorithm and its cost function (roughly, the points' distance from their cluster centre), which he says is called "...
2answers
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### How to make clusters (consisting of demands) equal to the load of a truck?

I am working on a routing problem where I have thousands of points (places) with individual demands (in Weight and Volume). So far I have created 5 clusters based on their location. Now I need to ...
1answer
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### K-means random initialization: two centers at same point

I'm wondering how k-means deals with randomly initializing two centers (for two distinct observations x_1 and x_2, but with the ...
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### K-means to Uncover Finite GMM Parameters in Population Case

Given a finite Gaussian mixture model with the number of distributions known, will k-means reveal the true mean parameters of each Gaussian in the infinite population case? I assume generally not, but ...
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### Clustering algorithm which is guaranteed to converge to global minimum [duplicate]

Well known k-means algorithm is not guaranteed to converge to global minimum. It only converges to local minimum. So my question is, what are the clustering algorithms that are guaranteed to converge ...
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### K-medians clustering WCS and BCS with Gmedian R package

I want to calculate the within-cluster sum of distances and between-cluster sum of distances for k-medians clustering. I see that the kGmedian function outputs the cluster centers and a vector of ...
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