Almost Linear Time Consistent Mode Estimation and Quick Shift Clustering
- URL: http://arxiv.org/abs/2503.07995v1
- Date: Tue, 11 Mar 2025 02:51:31 GMT
- Title: Almost Linear Time Consistent Mode Estimation and Quick Shift Clustering
- Authors: Sajjad Hashemian,
- Abstract summary: We propose a method for density-based clustering in high-dimensional spaces that combines Locality-Sensitive Hashing (LSH) with the Quick Shift algorithm.<n>The proposed approach achieves almost linear time complexity while preserving the consistency of density-based clustering.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: In this paper, we propose a method for density-based clustering in high-dimensional spaces that combines Locality-Sensitive Hashing (LSH) with the Quick Shift algorithm. The Quick Shift algorithm, known for its hierarchical clustering capabilities, is extended by integrating approximate Kernel Density Estimation (KDE) using LSH to provide efficient density estimates. The proposed approach achieves almost linear time complexity while preserving the consistency of density-based clustering.
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) - Local Sample-weighted Multiple Kernel Clustering with Consensus
Discriminative Graph [73.68184322526338]
Multiple kernel clustering (MKC) is committed to achieving optimal information fusion from a set of base kernels.
This paper proposes a novel local sample-weighted multiple kernel clustering model.
Experimental results demonstrate that our LSWMKC possesses better local manifold representation and outperforms existing kernel or graph-based clustering algo-rithms.
arXiv Detail & Related papers (2022-07-05T05:00:38Z) - A density peaks clustering algorithm with sparse search and K-d tree [16.141611031128427]
Density peaks clustering algorithm with sparse search and K-d tree is developed to solve this problem.
Experiments are carried out on datasets with different distribution characteristics, by comparing with other five typical clustering algorithms.
arXiv Detail & Related papers (2022-03-02T09:29:40Z) - Density Ratio Estimation via Infinitesimal Classification [85.08255198145304]
We propose DRE-infty, a divide-and-conquer approach to reduce Density ratio estimation (DRE) to a series of easier subproblems.
Inspired by Monte Carlo methods, we smoothly interpolate between the two distributions via an infinite continuum of intermediate bridge distributions.
We show that our approach performs well on downstream tasks such as mutual information estimation and energy-based modeling on complex, high-dimensional datasets.
arXiv Detail & Related papers (2021-11-22T06:26:29Z) - Density-Based Clustering with Kernel Diffusion [59.4179549482505]
A naive density corresponding to the indicator function of a unit $d$-dimensional Euclidean ball is commonly used in density-based clustering algorithms.
We propose a new kernel diffusion density function, which is adaptive to data of varying local distributional characteristics and smoothness.
arXiv Detail & Related papers (2021-10-11T09:00:33Z) - Fast Density Estimation for Density-based Clustering Methods [3.8972699157287702]
Density-based clustering algorithms are widely used for discovering clusters in pattern recognition and machine learning.
The robustness of density-based algorithms is heavily dominated by finding neighbors and calculating the density of each point which is time-consuming.
This paper proposes a density-based clustering framework by using the fast principal component analysis, which can be applied to density based methods to prune unnecessary distance calculations.
arXiv Detail & Related papers (2021-09-23T13:59:42Z) - Scalable Hierarchical Agglomerative Clustering [65.66407726145619]
Existing scalable hierarchical clustering methods sacrifice quality for speed.
We present a scalable, agglomerative method for hierarchical clustering that does not sacrifice quality and scales to billions of data points.
arXiv Detail & Related papers (2020-10-22T15:58:35Z) - SDCOR: Scalable Density-based Clustering for Local Outlier Detection in
Massive-Scale Datasets [0.0]
This paper presents a batch-wise density-based clustering approach for local outlier detection in massive-scale datasets.
Evaluations on real-life and synthetic datasets demonstrate that the proposed method has a low linear time complexity.
arXiv Detail & Related papers (2020-06-13T11:07:37Z) - Local Graph Clustering with Network Lasso [90.66817876491052]
We study the statistical and computational properties of a network Lasso method for local graph clustering.
The clusters delivered by nLasso can be characterized elegantly via network flows between cluster boundary and seed nodes.
arXiv Detail & Related papers (2020-04-25T17:52:05Z) - A fast and efficient Modal EM algorithm for Gaussian mixtures [0.0]
In the modal approach to clustering, clusters are defined as the local maxima of the underlying probability density function.
The Modal EM algorithm is an iterative procedure that can identify the local maxima of any density function.
arXiv Detail & Related papers (2020-02-10T08:34:16Z)
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.