Clustering via torque balance with mass and distance
- URL: http://arxiv.org/abs/2004.13160v1
- Date: Mon, 27 Apr 2020 20:34:06 GMT
- Title: Clustering via torque balance with mass and distance
- Authors: Jie Yang and Chin-Teng Lin
- Abstract summary: We propose a novel clustering method based on two natural properties of the universe: mass and distance.
The concept of torque describing the interactions of mass and distance forms the basis of the proposed parameter-free clustering algorithm.
- Score: 39.51621514760641
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Grouping similar objects is a fundamental tool of scientific analysis,
ubiquitous in disciplines from biology and chemistry to astronomy and pattern
recognition. Inspired by the torque balance that exists in gravitational
interactions when galaxies merge, we propose a novel clustering method based on
two natural properties of the universe: mass and distance. The concept of
torque describing the interactions of mass and distance forms the basis of the
proposed parameter-free clustering algorithm, which harnesses torque balance to
recognize any cluster, regardless of shape, size, or density. The gravitational
interactions govern the merger process, while the concept of torque balance
reveals partitions that do not conform to the natural order for removal.
Experiments on benchmark data sets show the enormous versatility of the
proposed algorithm.
Related papers
- Clustering Based on Density Propagation and Subcluster Merging [92.15924057172195]
We propose a density-based node clustering approach that automatically determines the number of clusters and can be applied in both data space and graph space.
Unlike traditional density-based clustering methods, which necessitate calculating the distance between any two nodes, our proposed technique determines density through a propagation process.
arXiv Detail & Related papers (2024-11-04T04:09:36Z) - Low-Energy Test of Quantum Gravity via Angular Momentum Entanglement [0.7499722271664147]
We investigate the interaction between the angular momenta of spherically-symmetric test masses considering a tree-level relativistic correction related to frame-dragging.
In this approach, the mass of the probes is not directly relevant; instead, their angular momentum plays the central role.
We show that significant quantum correlations can still arise between two rotating systems even when each is entangling in an eigenstate of rotation.
arXiv Detail & Related papers (2024-09-02T16:39:33Z) - HeNCler: Node Clustering in Heterophilous Graphs through Learned Asymmetric Similarity [55.27586970082595]
HeNCler is a novel approach for Heterophilous Node Clustering.
We show that HeNCler significantly enhances performance in node clustering tasks within heterophilous graph contexts.
arXiv Detail & Related papers (2024-05-27T11:04:05Z) - Gravity Mediated Entanglement between Oscillators as Quantum
Superposition of Geometries [0.0]
Protocols for observing gravity induced entanglement typically comprise the interaction of two particles.
In both cases the appearance of entanglement, within linearised quantum gravity, is due to gravity being in a highly non-classical state.
We conclude that the two usual protocols are due to gravity being in a highly non-classical state.
arXiv Detail & Related papers (2023-09-28T10:07:43Z) - Data-driven discovery of non-Newtonian astronomy via learning
non-Euclidean Hamiltonian [23.309368900269565]
We present a method for data-driven discovery of non-Newtonian astronomy.
Preliminary results show the importance of both these properties in training stability and prediction accuracy.
arXiv Detail & Related papers (2022-09-30T20:59:42Z) - Continuous-Variable Entanglement through Central Forces: Application to
Gravity between Quantum Masses [4.362023116605902]
We show that entanglement in such experiments is sensitive to initial relative momentum only when the system evolves into non-Gaussian states.
From a quantum information perspective, the results find applications as a momentum witness of non-Gaussian entanglement.
arXiv Detail & Related papers (2022-06-26T15:07:14Z) - Fast Simultaneous Gravitational Alignment of Multiple Point Sets [82.32416743939004]
This paper proposes a new resilient technique for simultaneous registration of multiple point sets by interpreting the latter as particle swarms rigidly moving in the mutually induced force fields.
Thanks to the improved simulation with altered physical laws and acceleration of globally multiply-linked point interactions, our Multi-Body Gravitational Approach (MBGA) is robust to noise and missing data.
In various experimental settings, MBGA is shown to outperform several baseline point set alignment approaches in terms of accuracy and runtime.
arXiv Detail & Related papers (2021-06-21T17:59:40Z) - Clustering Ensemble Meets Low-rank Tensor Approximation [50.21581880045667]
This paper explores the problem of clustering ensemble, which aims to combine multiple base clusterings to produce better performance than that of the individual one.
We propose a novel low-rank tensor approximation-based method to solve the problem from a global perspective.
Experimental results over 7 benchmark data sets show that the proposed model achieves a breakthrough in clustering performance, compared with 12 state-of-the-art methods.
arXiv Detail & Related papers (2020-12-16T13:01:37Z) - Fast Gravitational Approach for Rigid Point Set Registration with
Ordinary Differential Equations [79.71184760864507]
This article introduces a new physics-based method for rigid point set alignment called Fast Gravitational Approach (FGA)
In FGA, the source and target point sets are interpreted as rigid particle swarms with masses interacting in a globally multiply-linked manner while moving in a simulated gravitational force field.
We show that the new method class has characteristics not found in previous alignment methods.
arXiv Detail & Related papers (2020-09-28T15:05:39Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.