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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How do I assign homes to hospitals based on locality? (clustering, kmeans?)

I have a large set of $(X)$ hospitals and $(Y)$ homes, where $(Y)$ is much larger than $(X)$, and their respective coordinates. Each hospital can handle any home within a 50 mile radius, and up to 10,...
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Tips on speeding up hierarchical linkage clustering algorithm

I implemented a hierarchical linkage algorithm for a set of 5,000 points. Each point is defined with a longitude and a latitude. I read about this algorithm here. These are the steps: Compute ...
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Group time series events into minimal amount of buckets

I'm trying to efficiently compute events in a time series by grouping them into buckets. My goal is to have as few buckets as possible. The constraint is that events within one bucket are all within a ...
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Is there an online/offline tool that can perform K-means/median, given an initial centroid from the user?

The types of problems I am trying to solve are as follows: Given a set of co-ordinates such as: (1,2), (3,3), (6,2), (7,1), a value of k such as k=3 and an initial set of centroids such as c1=(2,2) ...
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Separating labelled points with a plane, minimizing label variance

Suppose we have observations with associated labels $\{({\bf x}_1, y_1), ({\bf x}_2, y_2), \dots, ({\bf x}_n, y_n)\}$ where ${\bf x}_i \in \mathbb{R}^d$ and $y_i \in \mathbb{R}$. Can we efficiently ...
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How does secondary clustering occur in hashing?

One of my friends said me that secondary clustering is the phenomenon occurring when the probe sequence has the same initial value. This definition shows that secondary clustering occurs in linear ...
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31 views

Question in coreset construction fro K-median clustering

I was reading Ke chen's paper about coreset construction for K-median clustering. In this paper, he assumed that $A$ is an $[α, β]$-bicriteria approximation for K-median clustering for some $α, β=O(1)$...
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building a unsupervised learning model to detect suspicious transactions using DBSCAN

I am working for the first time on building a unsupervised learning model to detect suspicious transactions using DBSCAN, Do I train the model on all data columns(Columns like account number, ...
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How to detect outliers using DBSCAN?

I am working on a Fraudulent Cash Transaction Detection System using DBSCAN and I want to know what is the proper way to identify outliers? Thank you ##Edite## I had a problem how to represent the ...
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35 views

How can I examine the subnetworks of a nearly fully connected graph?

I have an almost fully connected graph in python with roughly 3k nodes and 9M edges. Each node in this graph is represented by a point in R3 and each edge represents the distance between them with a ...
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What does the decision boundary of XOR problem look like?

My textbook walks through an example of solving the XOR problem in machine learning using a two-dimensional RBF network. It does this by setting the centers for the two basis functions at [0,0] and [1,...
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How to cluster a dataset in which each data point is composed of a set of 2-dimensional coordinates

I have a dataset with totally $1000$ scenarios, each of which is composed of $5$ users' coordinates $(x_i,y_i), \forall i \in \{1,\dots,5\}$. Now, based on users' coordinates, I want to cluster these $...
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How to calculate delta Q (modularity increase matrix) in graphs?

I've been trying to implement the Three-stage Algorithm to compare its results with our new proposed algorithm with different datasets than those mentioned in the article. I've succeeded in ...
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225 views

what would be the time complexity of DBSCAN algorithm?

what would be the time complexity of DBSCAN algorithm if we use it for graph(sparse) clustering $O(n^2)$ or $O(n \log{n})$?
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How to group intervals which overlap by some amount?

I have an algorithm that generates a list of intervals. The algorithm is run m times. Lets mark the intervals as tuples (s1, e1), (s2, e2), .., (sn, en). It is ...
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27 views

Clustering Application with a Huge Number of Clusters

I am wondering if there are any clustering applications in practice where the number of clusters, i.e., the $k$ in the $k$-means problem is very high ($k>50$, optimally $k>200$), if possible ...
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23 views

Finding the middle point of the “most populated” area in a set of points?

I'm working on a game-related application, and I'm trying to find the middle point of the most populated area in my map. Example: ...
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Clustering sets by set difference

Suppose you have $n$ nonequal sets $S_1, \ldots, S_n$ and some constant $0 \le k < n$. The goal of set clustering is to find a partition of the set $\mathbf{S} = \{S_1, \ldots, S_n\}$ such that the ...
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Bounding 0-1 matrix with k unique rows

Problem Statement: Suppose that I have a $0-1$ matrix $A$ (all of the entries are $0$ or $1$). I wish to find the tightest upper bound with $k$ many unique rows. To be more precise, let S denote the ...
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RPI cluster performance related to network performance

I'm writing my thesis and i have built a RPI cluster, containing 10 nodes which consists of RPI model 3b. I've them connected to two gigabit switches. I don't know the CAT of the cables. They are not ...
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How to find clusters of a set of points in n-dimensional space that are furthest from unwanted points

I have a list of 25 points and their coordinates in a 512-dimensional space. I have 8 target points and 17 points I need to avoid (the 17 points to avoid also have differences in priority of how ...
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Delivery Clustering based-on Pick-Drop & Driver locations

I'm reading some papers about Delivery Clustering to solve following generic problems: Given N orders (with its Pick-up and Drop location point) and M delivery-man (with his location). We are going ...
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How can one algorithmically define the required amount of centroids in K-Means clustering?

Say I have a dataset of n vectors. These are, by nature, clustered so that there is a significant distance difference between any two points within a cluster and any two points in separate clusters. ...
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How to cluster N sets into N subsets ,so that we can determine which set a point is from by checking its nearest neighbor in aforementioned subsets?

Question 1: Given N sets of points $S_1$ ... $S_n$ (no intersection between $S_i$ and $S_j$ when i != j), I want to find subsets of $S_1$ ... $S_n$ (call them $T_1$ ... $T_n$ respectively) So that ...
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Clustering customer with string data

I'm looking for a customer clustering solution. I have done a lot of research on the machine learning level to find algorithms that could fit my needs but I can't find much information when the data ...
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35 views

Clustering algorithm with specific cluster grouping areas

I'm looking for a way to cluster points in a given space, where clusters form around specific closed, allowed zones of that initial space. Each allowed zone should be surrounded by points of its ...
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Spliting strings into groups of similar strings

I would like to group a list of strings into groups of strings differing by max 1 character: For instance, given: [John, Alibaba, Johny, Alidaba, Mary] I would ...
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Finding fewest strings that cover $\Sigma^n$ up to $R$ edit operations

Let $\Sigma$ be the alphabet, $0<R<n$ be an integer and let $\Sigma^n$ denote the set of all strings of length $n$ over the alphabet. The task is to find the minimum $m$ such that there exist ...
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227 views

Proof for clustering in a network of friendship

Consider an undirected graph $G = (V, E)$ representing the social network of friendship/trust between students. We would like to form teams of three students that know each other. The question is to ...
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Algorithm to separate single contour of glyph into several strokes?

A glyph contour contains points set {p}, a point contains tuple (x,y,on_curve). Now, think about this need, converting contour of glyph X, for example, into to two contour parts or two strokes, point ...
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Is there a well-known algorithm for arranging inputs according to their category?

Background: (I’m a complete beginner in computer science in general, so I do apologise if my question is not formulated in a sensible way. E.g. I have avoided technicalities in formulating my ...
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Determine the optimal value of K - K-means clustering

I am trying to determine the optimal value of k. I have used a graph to plot the SSE against the values of k. Using the elbow method, what would be the optimal value of k? I am unsure as to whether 7 ...
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2 stage clustering

The problem I am facing is clustering problem, needed for a Vehicle routing problem (VRP) I'm tackling. It is a heterogeneous VRP with Time Windows and a capacity utilization constraint, i.e. a truck ...
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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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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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390 views

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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789 views

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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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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31 views

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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1k views

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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174 views

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 ...