Questions tagged [clustering]

Clustering is the problem of finding groups of data points (often modelled as nodes in a graph) that are closer to each other than to other points.

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Separate overlapping clusters

Suppose I have multiple data points in 2d (x,y) that are either labeled as A, B, C, or D. I find a minimum bounding area for points that are labeled as A and refer to it as cluster A. I can do the ...
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How can I express the similarity between a Bing and a Google search result?

I'm working on a "semantic" browser engine where all search engines should look the same. One way to do this is to hard-code parsing rules for each site; another is to use machine-learning. Of course ...
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Divide directed weighted graph into two parts

I have a directed, weighted graph $G = (E,V)$. For example, one might be $|E| = 74000, |G| = 725$. I want to divide this graph into two parts/clusters/communities, taking the edge weights into ...
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Weighted graph clustering with maximum size constraint

I'm currently trying to solve a clustering problem. I need to cluster/partition an undirected weighted graph into groups that are restricted to size n. I have ...
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Clustering - Complete Linkage draw example

I'm studying unsupervised learning methods (clustering) and i've seen the Complete Linkage Method. I've also seen the following statement: Unlike single linkage, the complete linkage method can be ...
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Find shared properties of a cluster samples

I have a dataset which contains ~15 features. With the elbow method, I found out that the optimal number of clusters is probably four. Therefore, I applied the K-means algorithm with four clusters. ...
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Point cloud clustering based on similarity in less than $O(n^2)$

not sure if this is the right place to ask this but here it goes. Let's assume I have some 2D points dataset consisting of facial landmarks, and I want to cluster these based on similarity so that I ...
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Evaluating clustering/partitioning quality

I'm wondering what are the most common/recognized methods to assess the quality of a clustering. That is because I have developed a tool that can cluster/partition a network (in this case, a public ...
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Why does the BFR (Bradley, Fayyad and Reina) algorithm assume clusters to be normally distributed around its centroid?

I'm following a course on data mining based on the lectures from Stanford University and the book Mining of massive datasets. On the topic of clustering, the BFR algorithm is explained with this ...
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Balanced $\epsilon$-separated partitioning by a hyperplane

Suppose we have $m$ points in $R^n$ and $\epsilon>0$ is a given constant. How can we find a hyperplane that the number of points that are $\epsilon$-close to it is minimum, with the constraint that ...
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What is difference between parallel virtual machine (PVM) and (mpi) Message passing Interface? [duplicate]

I am using beowulf cluster. And want to know the difference between parallel virtual machine(pvm) and message passing interface(mpi). Thank you.
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What is parallel virtual machine (pvm) and how it's different from mpi (message passing interface)

I am learning beowulf cluster. And I want to know what is pvm and how it's work and there is any difference between mpi and pvm
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What is the difference between parallel virtual machine and message passing interface. And how both works [duplicate]

I m learning beowulf system architecture and I encounter these terms many time. I know these are the library file and we use these to write parallel programing. But I do not know how these work and ...
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How to cluster images based on meta-information in tags

Context and Motivation I have researched online for an algorithm (independent of a programming language AND in the context of Machine Learning) that accepts images as inputs with the expectation that ...
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Grouping/Clustering results based on shared values

I have a data set that is similar to this: 10,000 jobs with 200,000 applicants which are linked to a job. I'm looking to cluster the shared jobs based on applicants that they share, am I reinventing ...
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How to perform particle swarm optimization clustering of results obtained from tf-idf vectorizer?

I have a collection of news articles. I have performed tf-idf operations on them. I am using python as programming language so it was just the use of TF-IDF vectorizer function. I now have the ...
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Cluster with categorical / ordinal

i have a dataset with movies review. I wish cluster my element but inside i have a categorical / ordinal values. i seen that exist: MCA (Multiple Correspondence Analysis) https://www.utdallas.edu/~...
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k-means clustered data: how to label newly incoming data

I have a data set with labels that were produced by a $k$-means clustering algorithm. Now there is some data (with the same data structure) from another source and I wonder what is the most sensible ...
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Algorithm to group entities into variable sized groups based on attribute

I have the following problem: There are n entities (persons) having a common attribute (weight) and I need to build weight categories (this is for a sports event) grouping people based on their ...
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Concrete/theoritical center of a cluster in a general metric space

What i call center is the point which minimize the distance to every points of a specific cluster. From what i know we can look for a concrete point in the cluster get an approximation of the center. ...
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Large-scale string clustering

I've more than 10 million strings of length 1-100 characters. This number will be even bigger in the future. I'm interested in clustering this data, but I'm not quite sure what would be effective at ...
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Variant of the “Stable Roommates Problem” when room has not 2 but “n” mates

I'm looking at the name of a variant of the Stable Roommates Problem, when the rooms have more than 2 mates, ie for example 6 to 8. Does this problem has a specific name? A well-known algorithm? To ...
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How fast can we optimally cluster 1-D data?

K-means clustering is the problem of partitioning a set of points in a metric space into $k$ sets (clusters), such that the sum of squared distances between each point and the center of its cluster) ...
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What is the relationship between pairwise loss and centroid loss?

What is the relationship between pairwise loss and centroid loss? Under what conditions you would expect them to give similar behavior? Under what conditions would they give different behavior?
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How to calculate the minimum number of groups, by grouping groups with capacity together?

I need to group cars (and their passengers) with other cars, and I don't know how to approach this problem. If I have, for example, 3 cars. Car A with 7 seats and 2 passengers (3/7 because of the ...
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Algorithms / heuristics for a distributed sorting problem

The setting: There's a cluster of $k$ computers (= nodes). For simplicity, assume their hardware is identical. The network topology can be complicated, but let's simplify and assume it's a clique ...
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Finding the “most modular” subset of graph vertices, i.e. that minimize disagreement inside and outside

Let $G = (V, E)$ be a graph. I want to find the subset of vertices of $G$ that minimizes a certain modularity cost. In our setting, the modularity cost of a subset $X$ is defined as the number of ...
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(DROP) Data Reduction Algorithm - How it works?

I am studing a PHD framework which the propose is to reduce the dataset with the most representative samples for training a classifier. Maybe I am loosing something, but I could not undestand a ...
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$k$ -center with outliers - but the points are on a line

The classic $k$-center with outliers problem is NP-hard and there exist approximation algorithms to solve it. However, what if we assume that the input point are on a line, rather than in an ...
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Creating Best Clusters of Objects Based on Distance Between Them

I have an array of images. And, there is a function that calculates the distance between two images. I wish to cluster the images based on this distance. So the clusters contain images that are all ...
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Identify objects (bus) on the map based on coordinates (lat, lon)

Let's say I have an android app that frequently sends current GPS location of the user. If person is driving with bus, I can easily get GPS location of the bus and display it on the map and update it ...
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Strict partitioning clustering of points in 2D space into variable (but fixed) length cluster in order to minimize distance from center

Given $\\N$ points in 2D space, one is required to cluster them into $\\M$ clusters, with each cluster of a given size $\\S_m$ such that $\sum S_m = N$, in order to minimize the sum of the distance of ...
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Clustering non-overlapping time series

I have thousands of times series of different length and different time. I want to group them together so that I know the optimal ones to pick as input for a ML algorithm and to document how they are ...
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Clustering via Max-Cut

I wonder if there are papers that uses max cut algorithm(s) to cluster data. For example, if an edge between two nodes $u$ and $v$ indicate that $u$ and $v$ are different, then the max-cut in some ...
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Python: Clustering based on pairwise distance matrix [closed]

I have a matrix which represents the distances between every two relevant items. For example, M[i][j] holds the distance between items i and j. My next aim is to cluster items by these distances. I ...
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What are the (efficient) algorithms to cluster squares into groups using a threshold such that the closest squares form groups?

I have a set of rectangles that needed to be grouped based on their locations. (All the rectangles follow the same orientation.) Two rectangles would be in the same group if the distance between them ...
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Spectral clustering algorithm doesn't use all available clusters

We have implemented a spectral clustering algorithm according to the article Multiclass Spectral Clustering (Yu and Shi, Proc. IEEE ICCV '03, vol. 2, pp. 313–319, 2003; PDF). We used ...