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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Efficiently find the distance from a point to the decision boundary for assigning points to a particular $k$-means cluster

I have run $k$-means on a large set of high-dimensional data, and now I want to find the distance from a point $x$ to the Voronoi cell associated with one of the $k$ centroids. (In a previous version ...
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Is $k$-means clustering strictly NP-hard?

I've had lectures and read other threads claiming that $k$-means clustering is NP-hard. The fact that they never mention NP-completeness makes me suspect that strict NP-hardness is what's meant. This ...
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What techniques are used to extract word usage patterns?

I am analyzing some text blobs at syntactical level. With a regular POS tagger one could get from a text the dependencies associated to a sentence. For example, given: In: ...
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1 answer
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K-Modes Clustering with Partially-Overlapping, Variable-Length Data?

I'm working on a project that's attempting to cluster books using machine learning. I'm using the K-Prototypes algorithm for clustering data that has both numerical and class-based data. Under the ...
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Grouping only overlapping points into stacks

I have n number of files on a ScrollView. Every time a file is added, x/y coordinates are stored in an array. The user is able to drag and drop the files anywhere they like. Overlapping files (e.g. ...
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Grouping/Clustering surface mesh triangles by function values

I have the following problem. I have triangular surface meshs of three dimensional bodies. For each triangle exists an associated function value. This value varies over the whole mesh, but there are ...
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Find clustering/partitioning based on edge weight

So I have a complete digraph with non-zero positive edge weights. The nodes represent locations and edge weights are the travel times between the two corresponding nodes. I have 2 or more agents ...
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What makes Spectral kmeans clustering better than only Kmeans clustering?

I know that Kmeans clustering is the final step of Spectral clustering. But why is it that the previous steps involved in Spectral clustering make it a more convenient clustering approach? moreover ...
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Find a dynamic programming solution that minimize the sum of the diameters of two clusters?

I asked a question at this link, where I suggested a greedy algorithm for this problem: Suppose given $2n$ points in the plane and we want partition points into two clusters $C_1$ , $C_2$ such that ...
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Proof of NP-hardness of the k-means clustering problem for $k\geqslant 3$

coming from the computing science side rather than from the data analysis one, I studied the k-means clustering problem for a short time and noticed that the NP-hardness of the problem for $k=2$ seems ...
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Is there an algorithm to efficiently generate all partitions of a set such that no cell contains fewer than k elements of the set?

I am trying to generate partitions of networks to evaluate clustering algorithms. I know that generating all partitions is infeasible (since they grow with Stirling number of the second kind which get ...
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Finding highly-connected regions of graphs

I have a large network of 10,000 nodes and I am trying to identify subgraphs which are clique-like, in that they share many connections. I don't a priori know how many subgraphs fit this criteria. To ...
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K-means, but normalized and with max

Given points $x_1, \ldots, x_n$ in the Euclidean space and $K \in \mathbb N$, I'm interested in the following objective. Partition the points into $K$ clusters $C_1, \ldots, C_K$ so that: $$\max_{i \...
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Cluster 3d points with constraints

I have some 3d point cloud I wish to cluster into some number of clusters. I have the probability of two points being in the same cluster given as some function of their relative locations, with the ...
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Efficient Network Clustering Algorithm for Million Node Networks

I am looking for a clustering algorithm that is scalable up to large sparse undirected, unweighted networks (10-40M nodes, 10-80M edges). The most important aspects I care about are scaling efficiency ...
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How should i face the cluster editing problem?

The mentioned problem: Cluster Editing Problem. I need to code this problem but i can't understand the algorithm behind it, even when i try to search for resources about graphs into the web; can ...
3 votes
1 answer
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A heuristic for finding the vector that is maximally distant from a set of vectors

I have two sets of vectors: A and B. I want to find the vector Bi in set ...
2 votes
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97 views

Minimize the sum of diameters of 2-clustering graph

Is there an algorithm with runtime $\mathcal{O}(n^2)$ that for given weighted graph $G$, partition it into 2 cluster $C_1,C_2$ such that sum of diameters of two clusters minimized? I find a paper with ...
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Formulate a 2-clustering problem in LP

The problem: Suppose there are $n$ points in plane, and we want to partition points into two clusters such that sum of diameter of clusters is minimized. The diameter of cluster is maximum distance ...
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Children of internal node in a quadtree with high dimensionality

Let's say for example we have 1000 points and 50 dimensions. And we build a quadtree where each node represents a 50-dimensional box and is divided by splitting the box into smaller boxes that are ...
2 votes
1 answer
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Understanding contradiction in proof of Algorithm for Testing of Clustering of points in metric space in sub-linear time

I am trying to understand this paper, in which (k, b)-clusterability is defined like so: A set $X$ of points in a metric space is (k, b)-diameter clusterable if $X$ can be partitioned into $k$ ...
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Testing of Clustering of points in metric space in sub-linear time

I am trying to understand this paper, in which (k, b)-clusterability is defined like so: A set $X$ of points in a metric space is (k, b)-diameter clusterable if $X$ can be partitioned into $k$ ...
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Clustering Formulas for Networks

Consider an undirected, unweighted graph 𝐺=(𝑉,𝐸). I want to compute the clustering coefficient of each node. In the publicly available lecture from stanford, the following formula for computing the ...
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Euclidean space vs metric space in density clustering algorithms

I'm trying to find out if these algorithm still work if i replace the Euclidean space with metric space defined on the input point set. But i'm having some trouble figuring it out for some of them. I ...
1 vote
1 answer
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Proof of approximation ratio for approximate triangle inequality version of k-center

Consider the standard $k$-center problem i.e find $k$ disks of radius $r$ that cover all points in a point set $P$. This problem has a well known greedy 2-approximation algorithm where you (...
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Initializations methods for lloyds algorithm (Kmeans++ vs Gonzalez)

I'm learning the initialization methods for Lloyd's algorithm. And I have a hard thing finding examples where Kmeans++ works better than Gonzalez and where the reverse is true so Gonzalez works better ...
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1 answer
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Possible partitions, for k-means problem(k=2)

I have this brute-force algorithm to solve the problem: Generate all possible partitions of P into two subsets of P1 and P2 For each partition P1, P2 generated in Step 1, compute the cost of the ...
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1 answer
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Hierarchial clustering cannonnical representation?

I have to handle large binary dataset. That is one of the reasons I have to build my own Hierarchical Clustering. As I digged into the algorithms I was surprised and not ;) to find that it is possible ...
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Optimal clustering with optimal number of clusters as well as max and min cluster size constraints

I need to cluster $N$ data points. I don't know the number of clusters to be formed. It needs to be found optimally. Also, there is maximum and minimum cluster size constraints, where $C_{\max}$ is ...
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Find Smooth Functions from Discrete Datas

I'm trying to write a algorithm to solve the following problem, which I did not find any related papers: Given a set of discrete data points generated by n unknown smooth functions $f_1(x), f_2(x), \...
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1 vote
1 answer
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How to group thousand of data points for each user

I have thousands of data points associated with users. So a single user can have 2000-10000 data points. These data points are identified by contiguous numbers (e.g. all numbers from 0 to 2000). Each ...
1 vote
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Is n-dimentional assignment problem for points NP-hard?

We have $n$ sets of $k$ points in $\mathbb R^d$ and we are trying to partition them to $k$ clusters of $n$ points such that from each set every point is mapped to a different cluster and the sum of ...
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Is there an approximation algorithm for the three-person stable roommates problem?

While there's an algorithm for solving the stable roommates problem, I understand that the three-people-per-room version of that problem, sometimes called the "threesome roommates problem", ...
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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,...
1 vote
1 answer
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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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2 votes
1 answer
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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) ...
2 votes
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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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1 vote
1 answer
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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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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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1 answer
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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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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 $...
2 votes
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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 ...
1 vote
1 answer
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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 $O(n \log{n})$?
4 votes
1 answer
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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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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 ...
1 vote
1 answer
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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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