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Cluster Forests
2013
Computational Statistics & Data Analysis
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With inspiration from Random Forests (RF) in the context of classification, a new clustering ensemble method---Cluster Forests (CF) is proposed. ...
The search for good local clusterings is guided by a cluster quality measure kappa. CF progressively improves each local clustering in a fashion that resembles the tree growth in RF. ...
However, the most direct motivation for our work is the Random Forests (RF) methodology for classification (Breiman, 2001) . ...
doi:10.1016/j.csda.2013.04.010
fatcat:tuscrqy2nzhgvelgbi4x5fmgei
Spectral Clustering, Bayesian Spanning Forest, and Forest Process
[article]
2022
arXiv
pre-print
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To fill this gap, we propose the Bayesian forest model as a generative graphical model for spectral clustering. ...
This is motivated by our discovery that the posterior connecting matrix in a forest model has almost the same leading eigenvectors, as the ones used by normalized spectral clustering. ...
In comparison, the clustering of the forest model shows much less sensitivity to model specification.
Pr(c i = c j | y) from the forest model.
(c) Pr(K | y) from the forest model. ...
arXiv:2202.00493v2
fatcat:2dqjt7pfabd5bkxkrv4ttan32i
Meaningful Clustered Forest: an Automatic and Robust Clustering Algorithm
[article]
2011
arXiv
pre-print
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the detection of less-populated, but still salient, clusters. ...
We propose a new clustering technique that can be regarded as a numerical method to compute the proximity gestalt. ...
Then, one can detect the meaningful clustered forest among non-background points, yielding a stabilized meaningful clustered forest. ...
arXiv:1104.0651v3
fatcat:xt2czxlgynhj5g4rg37wp546qy
The Sum-over-Forests clustering
2014
The European Symposium on Artificial Neural Networks
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This work introduces a novel way to identify dense regions in a graph based on a mode-seeking clustering technique, relying on the Sum-Over-Forests (SoF) density index [1] (which can easily be computed ...
Experiments on articial and real datasets show that the proposed index performs well in nodes clustering. ...
→ k is present in forest ϕ. ...
dblp:conf/esann/SenelleSF14
fatcat:pzkopqhscbaizigos2o4uln5be
Fair Correlation Clustering in Forests
[article]
2023
arXiv
pre-print
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We give an overview of the distributions and types of forests where Fair Correlation Clustering turns from tractable to intractable. ...
The unfair version of Correlation Clustering is trivial on forests, but adding fairness creates a surprisingly rich picture of complexities. ...
If G is a forest, then no cluster in a minimum-cost fair clustering is of size more than 4. Proof. ...
arXiv:2302.11295v1
fatcat:hcvboisgwbfbvovoo2k6r2gmse
Differentiable Clustering with Perturbed Spanning Forests
[article]
2023
arXiv
pre-print
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2023 pre-print arXiv:2305.16358v2 fatcat:b4d7hkmberdkdhz6y4cxpkgrbmOther Versions
We introduce a differentiable clustering method based on stochastic perturbations of minimum-weight spanning forests. ...
This allows us to include clustering in end-to-end trainable pipelines, with efficient gradients. ...
Acknowledgements We thank Jean-Philippe Vert for discussions relating to this work and Simon Legrand for conversations relating to the CLEPS computing cluster. ...
arXiv:2305.16358v3
fatcat:xifm3vtl4ff55og6axex34jslu
Forest Fire Clustering for Single-cell Sequencing with Iterative Label Propagation and Parallelized Monte Carlo Simulation
[article]
2022
arXiv
pre-print
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Overall, Forest Fire Clustering is a useful tool for rare cell type discovery in large-scale single-cell analysis. ...
Here, we introduce Forest Fire Clustering, an efficient and interpretable method for cell-type discovery from single-cell data. ...
Benchmarking Forest Fire Clustering on MCA data: a) Forest Fire cluster labels. b) Posterior exclusion probability for the Forest Fire cluster labels. c) Entropy for the Forest Fire cluster labels. d) ...
arXiv:2103.11802v4
fatcat:lhmjndie5bffzk6jhnyb3jddbm
Feature-Weighting and Clustering Random Forest
2020
International Journal of Computational Intelligence Systems
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Classical random forest (RF) is suitable for the classification and regression tasks of high-dimensional data. ...
In this paper, a novel method of node split of the decision trees is proposed, which adopts feature-weighting and clustering. ...
Table 5 5 The accuracy of FWCRF compared with Adaboost and random forest (RF). ...
doi:10.2991/ijcis.d.201202.001
fatcat:qfbfbgex7rdwtlkt37o26n6ume
Randomized Clustering Forests for Image Classification
2008
IEEE Transactions on Pattern Analysis and Machine Intelligence
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We introduce Extremely Randomized Clustering Forests-ensembles of randomly created clustering trees-that are more accurate, much faster to train and test, and more robust to background clutter compared ...
Finally, we show that our ERC-Forests are used very successfully for learning distances between images of never-seen objects. ...
Combining these ideas, we introduce Extemely Randomized Clustering Forests (ERC-Forests), which are ensembles of randomly created clustering trees. ...
doi:10.1109/tpami.2007.70822
pmid:18617720
fatcat:ylezgvgx6ze5lpl56bsfm7lclu
Boundary-Forest Clustering: Large-Scale Consensus Clustering of Biological Sequences
[article]
2020
bioRxiv
pre-print
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Here, we present Boundary-Forest Clustering (BFClust), a method that addresses these challenges in three main steps: 1) The approximate-nearest-neighbor retrieval method Boundary-Forest is used as a representative ...
selection step; 2) Downstream clustering of the representatives is performed using Markov Clustering (MCL); 3) Consensus clustering is applied across the Boundary-Forest, improving clustering accuracy ...
one clustering output, the whole 162 Boundary-Forest thus leading to an ensemble of possible clustering outputs. ...
doi:10.1101/2020.04.28.065870
fatcat:nh4hhmyqn5etjkmrcdmpjbfxxe
Forest Fire Prediction Using K-Mean Clustering and Random Forest Classifier
2022
CSRID Journal
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Based on the clustered data, the data training and data test given for random forest classifier for model development, the composition of the data training and data test is 70:30. ...
The clustering process needed to give a label to the data with five class label, very low risk, low risk, medium risk, high risk, very high risk. ...
Using K-Mean method that data have been clustered to be 5 cluster and produce below cluster.
D. Random Forest Classifier Random forest is one of the methods used for classification and regression. ...
doi:10.22303/csrid.14.2.2022.157-165
fatcat:lnz33g62f5f4rll66mzq5jgbly
Higher order clustering of Lyα forest
[article]
2023
arXiv
pre-print
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Higher order clustering statistics of Lyα forest provide a unique probe to study non-gaussianity in Intergalactic matter distribution up to high redshifts and from large to small scales. ...
The observational side of this involves redshift-space clustering of low-z (z<0.48) and high-z (1.7<z<3.5) Lyα absorbers. ...
Higher order clustering, however, remains largely unexplored in Lyα forest. ...
arXiv:2312.15761v1
fatcat:epwib6osa5hw5de6tsgsxyg33y
Combining clustering of variables and feature selection using random forests
[article]
2018
arXiv
pre-print
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Numerical performances of the proposed approach are compared with direct applications of random forests and variable selection using random forests on the original p variables. ...
The novel methodology proposed in this paper combines clustering of variables and feature selection. ...
Rectangles indicate the partition in K * = 9 clusters (in green, the m = 6 clusters selected by VSURF, and in red the remaining unrelevant clusters). • CoV/RF: random forests are applied on the K * synthetic ...
arXiv:1608.06740v2
fatcat:5q2gwadarvev5almvph4cxc3vm
Fuzzy clustering based on Forest optimization algorithm
2018
Journal of King Saud University: Computer and Information Sciences
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In this paper, the combination of one of the recent optimization algorithms called Forest optimization algorithm and one of the local search methods called gradient method are used to perform fuzzy clustering ...
By analyzing and comparing the results of the proposed method with the results of algorithms GGAFCM (fuzzy clustering based on genetic algorithm) and PSOFCM (fuzzy clustering based on particle swarm optimization ...
Using the Forest optimization algorithm, optimized cluster centers can be obtained. ...
doi:10.1016/j.jksuci.2016.09.005
fatcat:4qlm6irwvralzgnkeuqystewva
Creating health typologies with random forest clustering
2010
The 2010 International Joint Conference on Neural Networks (IJCNN)
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(ii) A novel random forest clustering algorithm is used for generating clusters and it has several obvious advantages over the commonly used k-means algorithm in practice. ...
As we use a novel random forest clustering algorithm which is immune to this kind of transformation in the clustering stage, it can retain the original flavor of the data as much as possible. ...
There are three parameters to be pre-specified for the random forest model: number of variables considered at each node split at tree construction, number of trees and number of forests. ...
doi:10.1109/ijcnn.2010.5596554
dblp:conf/ijcnn/SunBFM10
fatcat:5sn7vgerp5fmhgx4sczqch2eju
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