A unified view of density-based methods for semi-supervised clustering and classification
Sign Up to like & getrecommendations! Published in 2019 at "Data Mining and Knowledge Discovery"
DOI: 10.1007/s10618-019-00651-1
Abstract: Semi-supervised learning is drawing increasing attention in the era of big data, as the gap between the abundance of cheap, automatically collected unlabeled data and the scarcity of labeled data that are laborious and expensive… read more here.
Keywords: classification; semi supervised; view; supervised clustering ... See more keywords
Semi-Supervised Clustering for Financial Risk Analysis
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DOI: 10.1007/s11063-021-10564-0
Abstract: Many methods have been developed for financial risk analysis. In general, the conventional unsupervised approaches lack sufficient accuracy and semantics for the clustering, and the supervised approaches rely on large amount of training data for… read more here.
Keywords: supervised clustering; financial risk; risk analysis; semi supervised ... See more keywords
Multiple graph semi-supervised clustering with automatic calculation of graph associations
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DOI: 10.1016/j.neucom.2020.12.002
Abstract: Abstract Multiple graph clustering is an important tool in data integration and data mining for graph-based data. The prediction and classification accuracy can be significantly improved by integrating information from multiple sources and data sets.… read more here.
Keywords: graph semi; semi supervised; supervised clustering; association ... See more keywords
Supervised clustering for single-cell analysis
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DOI: 10.1038/s41592-019-0534-4
Abstract: A widely used concept from machine learning is put to use for single-cell analysis read more here.
Keywords: single cell; cell analysis; clustering single; supervised clustering ... See more keywords
Human-supervised clustering of multidimensional data using crowdsourcing
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DOI: 10.1098/rsos.211189
Abstract: Clustering is a central task in many data analysis applications. However, there is no universally accepted metric to decide the occurrence of clusters. Ultimately, we have to resort to a consensus between experts. The problem… read more here.
Keywords: using crowdsourcing; data using; clustering multidimensional; human supervised ... See more keywords
On the Use of Supervised Clustering in Stochastic NMPC Design
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Transactions on Automatic Control"
DOI: 10.1109/tac.2020.2970424
Abstract: In this article, a supervised clustering-based heuristic is proposed for the real-time implementation of approximate solutions to stochastic nonlinear model predictive control frameworks. The key idea is to update online a low cardinality set of… read more here.
Keywords: clustering stochastic; control; stochastic nmpc; nmpc design ... See more keywords
Semi-Supervised Clustering Under a “Compact-Cluster” Assumption
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Transactions on Knowledge and Data Engineering"
DOI: 10.1109/tkde.2022.3145347
Abstract: Semi-supervised clustering (SSC) aims to improve clustering performance with the support of prior knowledge (i.e., side information). Compared with pairwise constraints, the partial labeling information is more natural to characterize the data distribution in a… read more here.
Keywords: information; semi supervised; compact cluster; cluster ... See more keywords
Semi-Supervised Clustering With Constraints of Different Types From Multiple Information Sources
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Transactions on Pattern Analysis and Machine Intelligence"
DOI: 10.1109/tpami.2020.2979699
Abstract: Semi-supervised clustering is one of important research topics in cluster analysis, which uses pre-given knowledge as constraints to improve the clustering performance. While clustering a data set, people often get prior constraints from different information… read more here.
Keywords: different types; information sources; semi supervised; supervised clustering ... See more keywords
A supervised clustering MCMC methodology for large categorical feature spaces
Sign Up to like & getrecommendations! Published in 2021 at "Statistical Methods in Medical Research"
DOI: 10.1177/09622802211009258
Abstract: There is a well-established tradition within the statistics literature that explores different techniques for reducing the dimensionality of large feature spaces. The problem is central to machine learning and it has been largely explored under… read more here.
Keywords: methodology; feature spaces; feature; categorical feature ... See more keywords
Early Identification of Undesirable Outcomes for Transport Accident Injured Patients Using Semi-Supervised Clustering.
Sign Up to like & getrecommendations! Published in 2019 at "Studies in health technology and informatics"
DOI: 10.3233/shti190764
Abstract: Identifying those patient groups, who have unwanted outcomes, in the early stages is crucial to providing the most appropriate level of care. In this study, we intend to find distinctive patterns in health service use… read more here.
Keywords: injured patients; semi; transport accident; accident injured ... See more keywords
LFSC: A linear fast semi-supervised clustering algorithm that integrates reference-bulk and single-cell transcriptomes
Sign Up to like & getrecommendations! Published in 2022 at "Frontiers in Genetics"
DOI: 10.3389/fgene.2022.1068075
Abstract: The identification of cell types in complex tissues is an important step in research into cellular heterogeneity in disease. We present a linear fast semi-supervised clustering (LFSC) algorithm that utilizes reference samples generated from bulk… read more here.
Keywords: cell; semi supervised; linear fast; fast semi ... See more keywords