# Questions tagged [clustering]

Clustering is an unsupervised learning problem. It deals with finding "clusters" or groups in a collection of unlabeled data. A cluster is therefore a collection of objects which are “similar” and are “dissimilar” to the objects belonging to other clusters.

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### clustering (lat,lng) pairs, with clusters having the same number of elements

Imagine you have a list of (lat,lng) pairs. You have k employees. And you want each employee to visit roughly the same number of places, making the least distance possible. I've tried to solve this ...
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### Scoring set of points based on clustering

I have a sparse set of points with unpredictable locations. I need a way of "scoring" each set of points such that clustering is rewarded. My working case is actually one dimensional, but a two ...
476 views

### Clustering of letters - what approach would give the best results?

I am working on letter recognition program. I have a text and divide it into letters, every single letter is written to separate file. Now I want to apply a clustering algorithm to these images to ...
471 views

### Euclidean-squared max-cut in low dimensions

Let $x_1, \ldots, x_n$ be points in the plane $\mathbb{R}^2$. Consider a complete graph with the points as vertices and with edge weights of $\|x_i - x_j\|^2$. Can you always find a cut of weight that ...
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### Computational complexity of clustering algorithms

My wish is to describe the time complexity of several clustering approaches. For example, suppose we have $n$ data points in $m$ dimensional space. Suppose further that the pairwise dissimilarity ...
417 views

### K-NN or matrix factorization for discovering correlated features?

I am looking to cluster users together in a database, with each user represented by a number of features that are both discrete and continuous in nature. "Similar" users should be clustered together ...
349 views

### Clustering formalizations other than K-means for separable data

Real world data sometimes has a natural number of clusters (trying to cluster it into a number of cluster lesser than some magic k will cause a dramatic increase the clustering cost). Today I attended ...