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Neighbor Embedding Graph Model for Clustering Ensemble

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Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/widm.1289openalex-extracted

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inferredcites →https://doi.org/10.1007/978-1-4757-0450-1openalex-extracted

Pattern Recognition with Fuzzy Objective Function Algorithms

inferredcites →https://doi.org/10.1007/978-3-642-36778-6_7openalex-extracted

Adaptive Evidence Accumulation Clustering Using the Confidence of the Objects’ Assignments

inferredcites →https://doi.org/10.1007/978-3-642-38562-9_26openalex-extracted

A Self-Supervised Framework for Clustering Ensemble

inferredcites →https://doi.org/10.1007/978-3-642-40395-8_23openalex-extracted

Consensus Clustering with Robust Evidence Accumulation

inferredcites →https://doi.org/10.1007/s10462-018-9642-2openalex-extracted

Clustering ensemble selection considering quality and diversity

inferredcites →https://doi.org/10.1007/s10489-018-01397-xopenalex-extracted

A fuzzy clustering ensemble based on cluster clustering and iterative Fusion of base clusters

inferredcites →https://doi.org/10.1007/s10994-015-5495-yopenalex-extracted

On some transformations of high dimension, low sample size data for nearest neighbor classification

inferredcites →https://doi.org/10.1016/j.compeleceng.2018.05.005openalex-extracted

A comparative study of clustering ensemble algorithms

inferredcites →https://doi.org/10.1016/j.cosrev.2018.01.003openalex-extracted

Cluster ensembles: A survey of approaches with recent extensions and applications

inferredcites →https://doi.org/10.1016/j.inffus.2021.09.003openalex-extracted

Clustering ensemble via structured hypergraph learning

inferredcites →https://doi.org/10.1016/j.ins.2021.07.028openalex-extracted

A decentralized algorithm for distributed ensemble clustering

inferredcites →https://doi.org/10.1016/j.knosys.2016.06.012openalex-extracted

kNN-IS: An Iterative Spark-based design of the k-Nearest Neighbors classifier for big data

inferredcites →https://doi.org/10.1016/j.knosys.2017.01.021openalex-extracted

A new general nearest neighbor classification based on the mutual neighborhood information

inferredcites →https://doi.org/10.1016/j.knosys.2019.06.015openalex-extracted

Adaptive melanoma diagnosis using evolving clustering, ensemble and deep neural networks

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Hybrid genetic model for clustering ensemble

inferredcites →https://doi.org/10.1016/j.knosys.2021.107604openalex-extracted

A new two-layer nearest neighbor selection method for kNN classifier

inferredcites →https://doi.org/10.1016/j.patcog.2008.09.013openalex-extracted

Clustering aggregation by probability accumulation

inferredcites →https://doi.org/10.1016/j.patcog.2019.107015openalex-extracted

Auto-weighted multi-view clustering via deep matrix decomposition

inferredcites →https://doi.org/10.1016/j.patcog.2022.109282openalex-extracted

Self-supervised semi-supervised nonnegative matrix factorization for data clustering

inferredcites →https://doi.org/10.1016/j.patrec.2016.06.023openalex-extracted

A feature weighted penalty based dissimilarity measure for k-nearest neighbor classification with missing features

inferredcites →https://doi.org/10.1016/s0167-7152(96)00213-1openalex-extracted

Connectivity of the mutual k-nearest-neighbor graph in clustering and outlier detection

inferredcites →https://doi.org/10.1073/pnas.1700770114openalex-extracted

Robust continuous clustering

inferredcites →https://doi.org/10.1109/access.2018.2855437openalex-extracted

A Survey of Clustering With Deep Learning: From the Perspective of Network Architecture

inferredcites →https://doi.org/10.1109/access.2021.3049157openalex-extracted

An Unsupervised Ensemble Clustering Approach for the Analysis of Student Behavioral Patterns

inferredcites →https://doi.org/10.1109/epia.2005.341287openalex-extracted

Weighting Cluster Ensembles in Evidence Accumulation Clustering

inferredcites →https://doi.org/10.1109/tcyb.2017.2702343openalex-extracted

Locally Weighted Ensemble Clustering

inferredcites →https://doi.org/10.1109/tcyb.2020.3034157openalex-extracted

Clustering Ensemble Based on Hybrid Multiview Clustering

inferredcites →https://doi.org/10.1109/tit.1967.1053964openalex-extracted

Nearest neighbor pattern classification

inferredcites →https://doi.org/10.1109/tkde.2009.159openalex-extracted

Completely Lazy Learning

inferredcites →https://doi.org/10.1109/tkde.2018.2818729openalex-extracted

Semi-Supervised Ensemble Clustering Based on Selected Constraint Projection

inferredcites →https://doi.org/10.1109/tkde.2018.2842191openalex-extracted

A Rapid Hybrid Clustering Algorithm for Large Volumes of High Dimensional Data

inferredcites →https://doi.org/10.1109/tkde.2019.2903410openalex-extracted

Ultra-Scalable Spectral Clustering and Ensemble Clustering

inferredcites →https://doi.org/10.1109/tkde.2019.2942029openalex-extracted

Co-clustering Ensembles based on Multiple Relevance Measures

inferredcites →https://doi.org/10.1109/tkde.2021.3068461openalex-extracted

Measuring Diversity in Graph Learning: A Unified Framework for Structured Multi-View Clustering

inferredcites →https://doi.org/10.1109/tnnls.2020.2984814openalex-extracted

Self-Paced Clustering Ensemble

inferredcites →https://doi.org/10.1109/tnnls.2020.3015795openalex-extracted

Fast and Effective Active Clustering Ensemble Based on Density Peak

inferredcites →https://doi.org/10.1109/tnnls.2021.3107354openalex-extracted

From Ensemble Clustering to Subspace Clustering: Cluster Structure Encoding

inferredcites →https://doi.org/10.1109/tpami.2005.113openalex-extracted

Combining multiple clusterings using evidence accumulation

inferredcites →https://doi.org/10.1109/tpami.2005.237openalex-extracted

Clustering ensembles: models of consensus and weak partitions

inferredcites →https://doi.org/10.1109/tsmc.2018.2876202openalex-extracted

Enhanced Ensemble Clustering via Fast Propagation of Cluster-Wise Similarities

inferredcites →https://doi.org/10.1126/science.1242072openalex-extracted

Clustering by fast search and find of density peaks

inferredcites →https://doi.org/10.1137/1.9781611972740.35openalex-extracted

A Mixture Model for Clustering Ensembles

inferredcites →https://doi.org/10.1137/1.9781611972818.29openalex-extracted

Nonparametric Bayesian Co-clustering Ensembles

inferredcites →https://doi.org/10.1137/s1064827595287997openalex-extracted

A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs

inferredcites →https://doi.org/10.1145/1015330.1015414openalex-extracted

Solving cluster ensemble problems by bipartite graph partitioning

inferredcites →https://doi.org/10.1145/1460797.1460800openalex-extracted

Weighted cluster ensembles

inferredcites →https://doi.org/10.1145/276698.276876openalex-extracted

Approximate nearest neighbors

inferredcites →https://doi.org/10.1201/9781315373515-22openalex-extracted

Cluster Ensembles: Theory and Applications

inferredcites →https://doi.org/10.1504/ijaacs.2015.069570openalex-extracted

An efficient clustering ensemble selection algorithm

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Finding Groups in Data: An Introduction to Cluster Analysis.

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Similarity Analysis of Small- and Medium-Sized Watersheds Based on Clustering Ensemble Model

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