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Related papers: On one-dimensional Cluster cluster model

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A prominent parameter in the context of network analysis, originally proposed by Watts and Strogatz (Collective dynamics of `small-world' networks, Nature 393 (1998) 440-442), is the clustering coefficient of a graph $G$. It is defined as…

Combinatorics · Mathematics 2016-11-21 Michael Gentner , Irene Heinrich , Simon Jäger , Dieter Rautenbach

We propose a dynamical scheme for the combined processes of fragmentation and merging as a model system for cluster dynamics in nature and society displaying scale invariant properties. The clusters merge and fragment with rates…

Statistical Mechanics · Physics 2009-11-11 Jesper Ferkinghoff-Borg , Mogens H. Jensen , Joachim Mathiesen , Poul Olesen

A random network model which allows for tunable, quite general forms of clustering, degree correlation and degree distribution is defined. The model is an extension of the configuration model, in which stubs (half-edges) are paired to form…

Probability · Mathematics 2012-07-31 Frank Ball , Tom Britton , David Sirl

We study the cluster size distribution of particles for a two-species exclusion process which involves totally asymmetric transport process of two oppositely directed species with stochastic directional switching of the species on a 1D…

Statistical Mechanics · Physics 2022-09-13 Jim Chacko , Sudipto Muhuri , Goutam Tripathy

In the context of clustering, we consider a generative model in a Euclidean ambient space with clusters of different shapes, dimensions, sizes and densities. In an asymptotic setting where the number of points becomes large, we obtain…

Machine Learning · Statistics 2009-09-15 Ery Arias-Castro

We study conserved one-dimensional models of particle diffusion, attachment and detachment from clusters, where the detachment rates decrease with increasing cluster size as gamma(m) ~ m^{-k}, k>0. Heuristic scaling arguments based on…

Statistical Mechanics · Physics 2009-11-13 F. D. A. Aarao Reis , R. B. Stinchcombe

This paper introduces a new clustering technique, called {\em dimensional clustering}, which clusters each data point by its latent {\em pointwise dimension}, which is a measure of the dimensionality of the data set local to that point.…

Machine Learning · Statistics 2018-05-29 Shohei Hidaka , Neeraj Kashyap

Many real-world networks exhibit scale-free feature, have a small diameter and a high clustering tendency. We have studied the properties of a growing network, which has all these features, in which an incoming node is connected to its…

Statistical Mechanics · Physics 2009-11-10 Parongama Sen , S. S. Manna

Clustering is a widely used unsupervised learning method for finding structure in the data. However, the resulting clusters are typically presented without any guarantees on their robustness; slightly changing the used data sample or…

Machine Learning · Statistics 2017-01-02 Andreas Henelius , Kai Puolamäki , Henrik Boström , Panagiotis Papapetrou

We study numerically a model of nonequilibrium networks where nodes and links are added at each time step with aging of nodes and connectivity- and age-dependent attachment of links. By varying the effects of age in the attachment…

Statistical Mechanics · Physics 2015-05-13 Nuno Crokidakis , Marcio Argollo de Menezes

We study the problem of clustering sequences of unlabeled point sets taken from a common metric space. Such scenarios arise naturally in applications where a system or process is observed in distinct time intervals, such as biological…

Data Structures and Algorithms · Computer Science 2017-10-17 Tamal K. Dey , Alfred Rossi , Anastasios Sidiropoulos

The ubiquitous occurrence of cluster patterns in nature still lacks a comprehensive understanding. It is known that the dynamics of many such natural systems is captured by ensembles of Stuart-Landau oscillators. Here, we investigate…

Pattern Formation and Solitons · Physics 2019-02-13 Felix P. Kemeth , Sindre W. Haugland , Katharina Krischer

A computational theory for clustering and a semi-supervised clustering algorithm is presented. Clustering is defined to be the obtainment of groupings of data such that each group contains no anomalies with respect to a chosen grouping…

Machine Learning · Computer Science 2025-07-17 Nassir Mohammad

We give a quantitative analysis of clustering in a stochastic model of one-dimensional gas. At time zero, the gas consists of $n$ identical particles that are randomly distributed on the real line and have zero initial speeds. Particles…

Probability · Mathematics 2008-06-17 Vladislav V. Vysotsky

The determination of cluster centers generally depends on the scale that we use to analyze the data to be clustered. Inappropriate scale usually leads to unreasonable cluster centers and thus unreasonable results. In this study, we first…

Machine Learning · Statistics 2016-10-20 Xiurui Geng , Hairong Tang

We develop a simple and unified approach to investigate several aspects of the cluster statistics of random expansive (multi-)sets. In particular, we determine the limiting distribution of the size of the smallest and largest clusters, we…

Probability · Mathematics 2022-08-02 Konstantinos Panagiotou , Leon Ramzews

We present a clustering method and provide a theoretical analysis and an explanation to a phenomenon encountered in the applied statistical literature since the 1990's. This phenomenon is the natural adaptability of the order when using a…

Statistics Theory · Mathematics 2022-03-23 Thierry Dumont

Let $X,X_1,X_2,\ldots$ be i.i.d. mean zero random vectors with values in a separable Banach space $B$, $S_n=X_1+\cdots+X_n$ for $n\ge1$, and assume $\{c_n:n\ge1\}$ is a suitably regular sequence of constants. Furthermore, let…

Probability · Mathematics 2014-03-28 Uwe Einmahl , Jim Kuelbs

We derive the finite size dependence of the clustering coefficient of scale-free random graphs generated by the configuration model with degree distribution exponent $2<\gamma<3$. Degree heterogeneity increases the presence of triangles in…

Disordered Systems and Neural Networks · Physics 2015-06-05 Pol Colomer-de-Simon , Marian Boguna

Let $(X_{n,i})_{1\le i\le n,n\in\mathbb{N}}$ be a triangular array of row-wise stationary $\mathbb{R}^d$-valued random variables. We use a "blocks method" to define clusters of extreme values: the rows of $(X_{n,i})$ are divided into $m_n$…

Statistics Theory · Mathematics 2020-05-19 Holger Drees , Holger Rootzén