Accelerating Infinite Ensemble of Clustering by Pivot Features
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DOI: 10.1007/s12559-018-9583-8
Abstract: The infinite ensemble clustering (IEC) incorporates both ensemble clustering and representation learning by fusing infinite basic partitions and shows appealing performance in the unsupervised context. However, it needs to solve the linear equation system with… read more here.
Keywords: accelerating infinite; pivot features; clustering pivot; ensemble clustering ... See more keywords
Ensemble clustering based on dense representation
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DOI: 10.1016/j.neucom.2019.04.078
Abstract: Abstract Ensemble clustering has emerged as a powerful tool for improving the stability and accuracy of the clustering task. Although various approaches have been proposed for improving the performance of algorithms, most of them ignored… read more here.
Keywords: method; based dense; clustering based; dense representation ... See more keywords
A Bayesian approach to ensemble clustering
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DOI: 10.1080/02331888.2025.2505576
Abstract: The paper presents a novel Bayesian approach to clustering techniques. A new method is proposed in an ensemble scheme for clustering, aimed at improving results in terms of robustness and interpretability. Our approach is organized… read more here.
Keywords: ensemble clustering; bayesian approach; approach; approach ensemble ... See more keywords
scBGEDA: deep single-cell clustering analysis via a dual denoising autoencoder with bipartite graph ensemble clustering
Sign Up to like & getrecommendations! Published in 2023 at "Bioinformatics"
DOI: 10.1093/bioinformatics/btad075
Abstract: Abstract Motivation Single-cell RNA sequencing (scRNA-seq) is an increasingly popular technique for transcriptomic analysis of gene expression at the single-cell level. Cell-type clustering is the first crucial task in the analysis of scRNA-seq data that… read more here.
Keywords: cell; autoencoder; scrna seq; analysis ... See more keywords
Stratified Feature Sampling for Semi-Supervised Ensemble Clustering
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DOI: 10.1109/access.2019.2939581
Abstract: Ensemble Clustering (EC), which seeks to generate a consensus clustering by integrating multiple base clusterings, has attracted increasing attentions. However, traditional EC methods typically have three main limitations: (1) High dimensional data present a huge… read more here.
Keywords: stratified feature; semi supervised; supervised ensemble; ensemble clustering ... See more keywords
Did They Sense it Coming? A Pipelined Approach for Tsunami Prediction Based on Aquatic Behavior Using Ensemble Clustering and Fuzzy Rule-Based Classification
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DOI: 10.1109/access.2020.3022865
Abstract: Tsunami is one of the real feelings of dread among humanity. Designing an early and effective Tsunami Warning System (TWS) is an immediate goal, for which the scientific community is working. Underwater seismic responses sensed… read more here.
Keywords: tsunami; sense coming; approach; coming pipelined ... See more keywords
An Unsupervised Ensemble Clustering Approach for the Analysis of Student Behavioral Patterns
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DOI: 10.1109/access.2021.3049157
Abstract: Specialized services and management must understand students’ behavioral patterns in a timely and accurate manner. Based on these patterns, we can make targeted rules, especially for unexpected patterns. To perform this type of work, a… read more here.
Keywords: student behavioral; analysis; ensemble clustering; student ... See more keywords
Double weighted ensemble clustering for cancer subtypes analysis
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DOI: 10.1109/access.2022.3167031
Abstract: The era of big data provides the possibility of precision medicine. The most important idea we have for cancer is to divide and treat. Theoretically, each person’s cancer should be different, so it is very… read more here.
Keywords: weighted ensemble; cancer; double weighted; analysis ... See more keywords
Enhanced Spectral Ensemble Clustering for Fault Diagnosis: Application to Photovoltaic Systems
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DOI: 10.1109/access.2024.3497977
Abstract: The role of clustering in unsupervised fault diagnosis is significant, but different clustering techniques can yield varied results and cause inevitable uncertainty. Ensemble clustering methods have been introduced to tackle this challenge. This study presents… read more here.
Keywords: ensemble clustering; fault diagnosis; spectral ensemble; application ... See more keywords
Tensorized Graph Learning for Spectral Ensemble Clustering
Sign Up to like & getrecommendations! Published in 2025 at "IEEE Transactions on Circuits and Systems for Video Technology"
DOI: 10.1109/tcsvt.2024.3492814
Abstract: Ensemble clustering based on co-association matrices integrates multiple connective matrices from base clusterings to achieve superior results. However, these methods primarily focus on inter-sample relationships, neglecting variations across different base clusterings, potentially introducing noise. Additionally,… read more here.
Keywords: tensorized graph; ensemble clustering; graph learning; spectral ensemble ... See more keywords
Ensemble Clustering With Attentional Representation
Sign Up to like & getrecommendations! Published in 2024 at "IEEE Transactions on Knowledge and Data Engineering"
DOI: 10.1109/tkde.2023.3292573
Abstract: Ensemble clustering has emerged as a powerful framework for analyzing heterogeneous and complex data. Despite the abundance of existing schemes, co-association matrix-based methods remain the mainstream approach. However, focusing solely on pairwise correlations falls short… read more here.
Keywords: method; ensemble clustering; representation; clustering attentional ... See more keywords