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Robust Clustering as Ensembles of Affinity Relations

Hairong Liu, Longin Jan Latecki, Shuicheng Yan
2010 Neural Information Processing Systems  
In this paper, we regard clustering as ensembles of k-ary affinity relations and clusters correspond to subsets of objects with maximal average affinity relations.  ...  The average affinity relation of a cluster is relaxed and well approximated by a constrained homogenous function.  ...  CSIDM-200803 partially funded by a grant from the National Research Foundation (NRF) administered by the Media Development Authority (MDA) of Singapore, and this work has also been partially supported  ... 
dblp:conf/nips/LiuLY10 fatcat:i2fzccpd7jehnffa5uzvw6dsr4

Ultra-Scalable Spectral Clustering and Ensemble Clustering

Dong Huang, Chang-Dong Wang, Jiansheng Wu, Jian-Huang Lai, Chee Keong Kwoh
2019 IEEE Transactions on Knowledge and Data Engineering  
In U-SENC, multiple U-SPEC clusterers are further integrated into an ensemble clustering framework to enhance the robustness of U-SPEC while maintaining high efficiency.  ...  This paper focuses on scalability and robustness of spectral clustering for extremely large-scale datasets with limited resources.  ...  The rest of the paper is organized as follows. The related work on large-scale spectral clustering and ensemble clustering is reviewed in Section 2.  ... 
doi:10.1109/tkde.2019.2903410 fatcat:brxe4ms6grctriea3ziaxetefe

Spectral Clustering via Ensemble Deep Autoencoder Learning (SC-EDAE) [article]

Severine Affeldt, Lazhar Labiod, Mohamed Nadif
2019 arXiv   pre-print
Extensive experiments on various benchmark datasets demonstrate the potential and robustness of our approach compared to state-of-the-art deep clustering methods.  ...  To alleviate the impact of such hyperparameters setting on the clustering performance, we propose a new model which combines the spectral clustering and deep autoencoder strengths in an ensemble learning  ...  The ensemble approach allows a better predictive performance and a more robust clustering as compared to the results obtained with a single model.  ... 
arXiv:1901.02291v2 fatcat:vzzjrn6345bsriymwnktejm4py

The Heterogeneous Cluster Ensemble Method Using Hubness for Clustering Text Documents [chapter]

Jun Hou, Richi Nayak
2013 Lecture Notes in Computer Science  
A heterogeneous cluster ensemble is constructed with multiple types of relations i.e. document-term, documentconcept and document-category.  ...  We propose a cluster ensemble method to map the corpus documents into the semantic space embedded in Wikipedia and group them using multiple types of feature space.  ...  The proposed novel Cluster Ensemble based Sequential Clustering using Hubness (CESC-H) method, integrating unsupervised cluster ensemble learning and hubness of documents, has the capability of clustering  ... 
doi:10.1007/978-3-642-41230-1_9 fatcat:qvczb6w3vbcepjbxtwbgv4glqy

Unsupervised Decision Forest for Data Clustering and Density Estimation [article]

Hayder Albehadili, Naz Islam
2015 arXiv   pre-print
The Random Forest method has been specifically applied to construct a robust affinity graph that provides information on the underlying structure of data objects used in clustering.  ...  The proposed method is also robust, and can discriminate between the complex features of data points among different clusters.  ...  [18] used a random forest to construct affinity graphs. The authors have proposed two subtle methods to construct robust affinity matrix and enhance clustering results.  ... 
arXiv:1507.04060v1 fatcat:kwy44it7xbfn3bmwazpfl2edmi

Ensembling Solutions for Semi – Supervised Clusters

Viveka Priya
2018 International Journal for Research in Applied Science and Engineering Technology  
robust, and exact outcomes to perform the clustering process.  ...  The proposed approaches function admirably on the greater part of this present reality datasets.  ...  RELATED DATA Semi-supervised clustering is one of the important research directions in the area of data mining, which is able to make use of prior knowledge, such as pairwise constraints or a small amount  ... 
doi:10.22214/ijraset.2018.3657 fatcat:3px3hpjvuzhxnpa3e2mizuxcva

The Core Cluster-Based Subspace Weighted Clustering Ensemble

Xuan Huang, Fang Qin, Lin Lin
2022 Wireless Communications and Mobile Computing  
In the ensemble process, the core clusters are viewed as the basic unit, and the stability of the cluster is evaluated by measuring the distance between the core cluster pairs, and the similarity between  ...  In order to reduce the complexity of the model, reduce the computational cost, and obtain a more robust clustering solution, we combine subspace clustering and ensemble learning to propose a novel subspace  ...  related results and analysis in Section 4, and finally, in Section 5, we conclude this paper. 2 Wireless Communications and Mobile Computing Related Work Clustering ensemble, also known as cluster  ... 
doi:10.1155/2022/7990969 doaj:660a63111d2b4032a799844fd48cec78 fatcat:epz4z2wf5vafnprqacqmew2skq

Robust Spectral Clustering Using Statistical Sub-Graph Affinity Model

Justin A. Eichel, Alexander Wong, Paul Fieguth, David A. Clausi, Jesus Gomez-Gardenes
2013 PLoS ONE  
To accommodate for image noise and textural characteristics, this study introduces the concept of sub-graph affinity, where each node in the primary graph is modeled as a sub-graph characterizing the neighborhood  ...  Unfortunately, the presence of image noise as well as textural characteristics can have a significant negative effect on the segmentation performance.  ...  [9] also utilize sub-graphs, they improve robustness by clustering graphs relating to the structure of objects, applied during the clustering step of spectral clustering.  ... 
doi:10.1371/journal.pone.0082722 pmid:24386111 pmcid:PMC3873262 fatcat:imtc3eamojh33odp5s2p3qet3a

Cluster Ensembles via Weighted Graph Regularized Nonnegative Matrix Factorization [chapter]

Liang Du, Xuan Li, Yi-Dong Shen
2011 Lecture Notes in Computer Science  
Cluster ensembles aim to generate a stable and robust consensus clustering by combining multiple different clustering results of a dataset.  ...  Traditional clustering ensemble algorithms learn the consensus clustering using either of the two representations, but not both.  ...  This work is supported in part by the National Natural Science Foundation of China (NSFC) grants 60970045 and 60833001.  ... 
doi:10.1007/978-3-642-25853-4_17 fatcat:bf76v7j32vgt5gvsp5a6ta6biy

Predicting MHC Class II Epitopes Using Separated Constructive Clustering Ensemble

Hossam Fathy ElSemellawy, Amr Badr, Mostafa Abdel Aziem
2012 American Journal of Bioinformatics Research  
Separated Constructive Clustering Ensemble (SCCE) is our new version for Constructive Clustering Ensemble (CCE) [27] .  ...  An implementation of MHCII-SCCE as an online web server for predicting MHC-II Epitopes is freely accessible at  ...  This means that peptides with log50k transformed binding affinity values greater than 0.426 are classified as binders and peptides with binding affinity values less than or equal 0.426 as classifier as  ... 
doi:10.5923/j.bioinformatics.20120202.01 fatcat:s5afya2jfbbebc73ldafpo3dye

Fast Multi-view Clustering via Ensembles: Towards Scalability, Superiority, and Simplicity [article]

Dong Huang, Chang-Dong Wang, Jian-Huang Lai
2022 arXiv   pre-print
In light of this, we propose a fast multi-view clustering via ensembles (FastMICE) approach.  ...  Then, a set of diversified base clusterings for different view groups are obtained via fast graph partitioning, which are further formulated into a unified bipartite graph for final clustering in the late-stage  ...  ACKNOWLEDGMENTS This project was supported by the NSFC (61976097, 61876193 & 62076258) and the Natural Science Foundation of Guangdong Province (2021A1515012203).  ... 
arXiv:2203.11572v1 fatcat:cckfvgu3tbbenap6ebjwsj3coq

Constraint based Cluster Ensemble to Detect Outliers in Medical Datasets

Visakh. R, Lakshmipathi.B Lakshmipathi.B
2012 International Journal of Computer Applications  
This work uses a form of semi-supervised cluster ensemble to analyze outlier patterns based on their relations to clusters.  ...  Specifically, the prior knowledge of a dataset is fed to the cluster ensemble in the form of constraints.  ...  Spectral clustering used in ensemble clustering framework helps to produce highly robust clusters.  ... 
doi:10.5120/6854-9393 fatcat:zh6hrwphh5hlnmi723upfpohau

LIBSA – A Method for the Determination of Ligand-Binding Preference to Allosteric Sites on Receptor Ensembles

Harrison J. Hocker, Nandini Rambahal, Alemayehu A. Gorfe
2014 Journal of Chemical Information and Modeling  
Model. 2014, 54, 530−538 a The data represent quantification of the ability of closely related ligand pairs to target a given pocket on the same set of Ras conformers, calculated as the ratio between the  ...  Ligands can then be triaged by their tendency to bind to a site instead of ranking by affinity alone.  ...  We have demonstrated the usefulness of this approach by applying it on a diverse set of known ligands and their receptors as well as a small set of related ligands docked onto a large ensemble of Ras conformers  ... 
doi:10.1021/ci400474u pmid:24437606 pmcid:PMC3985772 fatcat:ncadunpqxnghrkmxb5ku4ikxmu

A Review: Comparative Analysis Of Different Categorical Data Clustering Ensemble Methods

S. Sarumathi, N. Shanthi, M. Sharmila
2014 Zenodo  
The main hope of the cluster ensemble is to merge different clustering solutions in such a way to achieve accuracy and to improve the quality of individual data clustering.  ...  This paper exposes the comparative study of different cluster ensemble methods along with their features, systematic working process and the average accuracy and error rates of each ensemble methods.  ...  In Table I , we summarized the previously denoted ensemble methods in relate to its highlighting features and limitations of each technique which are as follows: A.  ... 
doi:10.5281/zenodo.1336474 fatcat:7uxe2jhtnzhhjfp5gx5iwuoyni

Fuzzyc-Means and Cluster Ensemble with Random Projection for Big Data Clustering

Mao Ye, Wenfen Liu, Jianghong Wei, Xuexian Hu
2016 Mathematical Problems in Engineering  
At the same time, a new cluster ensemble approach based on FCM clustering with random projection is also proposed.  ...  Experimental results reveal the efficiency, effectiveness, and robustness of our algorithm compared to the state-of-the-art methods.  ...  As a result, affinity matrix obtained here is the same as the one of standard spectral embedding, and our output is just the partition result of standard spectral clustering.  ... 
doi:10.1155/2016/6529794 fatcat:uzt7dnyfsrge7d77faoqbj7u4y
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