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Efficient dataset size reduction by finding homogeneous clusters

https://doi.org/10.1145/2371316.2371349
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What this work says

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

inferredcites →https://doi.org/10.1007/978-3-540-24844-6_87openalex-snapshot
Comparison of Instance Selection Algorithms II. Results and Comments
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Comparison of Instances Seletion Algorithms I. Algorithms Survey
inferredcites →https://doi.org/10.1007/s10044-008-0142-xopenalex-snapshot
A new fast prototype selection method based on clustering
inferredcites →https://doi.org/10.1007/s10462-010-9165-yopenalex-snapshot
A review of instance selection methods
inferredcites →https://doi.org/10.1016/0167-8655(96)00041-4openalex-snapshot
A sample set condensation algorithm for the class sensitive artificial neural network
inferredcites →https://doi.org/10.1016/j.patcog.2003.12.012openalex-snapshot
High training set size reduction by space partitioning and prototype abstraction
inferredcites →https://doi.org/10.1016/j.patcog.2006.10.018openalex-snapshot
Self-generating prototypes for pattern classification
inferredcites →https://doi.org/10.1016/s0031-3203(00)00184-9openalex-snapshot
An incremental prototype set building technique
inferredcites →https://doi.org/10.1016/s0031-3203(01)00208-4openalex-snapshot
An efficient prototype merging strategy for the condensed 1-NN rule through class-conditional hierarchical clustering
inferredcites →https://doi.org/10.1023/a:1007626913721openalex-snapshot
Reduction Techniques for Instance-Based Learning Algorithms
inferredcites →https://doi.org/10.1023/a:1014043630878openalex-snapshot
Advances in Instance Selection for Instance-Based Learning Algorithms
inferredcites →https://doi.org/10.1023/a:1022689900470openalex-snapshot
Instance-Based Learning Algorithms
inferredcites →https://doi.org/10.1109/icpr.2006.1119openalex-snapshot
The Generalized Condensed Nearest Neighbor Rule as A Data Reduction Method
inferredcites →https://doi.org/10.1109/t-c.1974.223827openalex-snapshot
Finding Prototypes For Nearest Neighbor Classifiers
inferredcites →https://doi.org/10.1109/tc.1980.1675485openalex-snapshot
IEEE Computer Society Press
inferredcites →https://doi.org/10.1109/tit.1968.1054155openalex-snapshot
The condensed nearest neighbor rule (Corresp.)
inferredcites →https://doi.org/10.1109/tit.1972.1054809openalex-snapshot
The reduced nearest neighbor rule (Corresp.)
inferredcites →https://doi.org/10.1109/tit.1975.1055464openalex-snapshot
An algorithm for a selective nearest neighbor decision rule (Corresp.)
inferredcites →https://doi.org/10.1109/tpami.2011.142openalex-snapshot
Prototype Selection for Nearest Neighbor Classification: Taxonomy and Empirical Study
inferredcites →https://doi.org/10.1109/tsmc.1972.4309137openalex-snapshot
Asymptotic Properties of Nearest Neighbor Rules Using Edited Data
inferredcites →https://doi.org/10.1109/tsmcc.2010.2103939openalex-snapshot
A Taxonomy and Experimental Study on Prototype Generation for Nearest Neighbor Classification

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Improving Data Reduction by Merging Prototypes
inferred← citeshttps://doi.org/10.1007/978-3-031-44192-9_27openalex-snapshot
LRP-GUS: A Visual Based Data Reduction Algorithm for Neural Networks
inferred← citeshttps://doi.org/10.1007/978-3-319-04939-7_14openalex-snapshot
EHC: Non-parametric Editing by Finding Homogeneous Clusters
inferred← citeshttps://doi.org/10.1007/978-3-319-09903-3_16openalex-snapshot
Applying Prototype Selection and Abstraction Algorithms for Efficient Time-Series Classification
inferred← citeshttps://doi.org/10.1007/978-3-319-44944-9_28openalex-snapshot
Efficient Support Vector Machine Classification Using Prototype Selection and Generation
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Generating Fixed-Size Training Sets for Large and Streaming Datasets
inferred← citeshttps://doi.org/10.1007/978-3-642-33409-2_34openalex-snapshot
A Fast Hybrid k-NN Classifier Based on Homogeneous Clusters
inferred← citeshttps://doi.org/10.1007/978-3-642-40728-4_5openalex-snapshot
Applying General-Purpose Data Reduction Techniques for Fast Time Series Classification
inferred← citeshttps://doi.org/10.1007/978-3-662-44845-8_36openalex-snapshot
WebDR: A Web Workbench for Data Reduction
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RHC: a non-parametric cluster-based data reduction for efficient $$k$$ k -NN classification
inferred← citeshttps://doi.org/10.1007/s10462-013-9411-1openalex-snapshot
Efficient $$k$$ k -NN classification based on homogeneous clusters
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Efficient editing and data abstraction by finding homogeneous clusters
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Instance reduction for supervised learning using input-output clustering method
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An approach of improving decision tree classifier using condensed informative data
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Towards an Analytical Definition of Sufficient Data
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A hybrid tuple selection pipeline for smartphone based Human Activity Recognition
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Improving the sustainability of WiFi-enabled indoor localization systems through meta-heuristic based instance selection approach
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Exploring the effect of data reduction on Neural Network and Support Vector Machine classification
inferred← citeshttps://doi.org/10.1016/j.patcog.2025.111884openalex-snapshot
Insights into imbalance-aware Multilabel Prototype Generation mechanisms for k -Nearest Neighbor classification in noisy scenarios
inferred← citeshttps://doi.org/10.1093/jigpal/jzv015openalex-snapshot
FHC: an adaptive fast hybrid method for k-NN classification
inferred← citeshttps://doi.org/10.1109/itsc57777.2023.10422460openalex-snapshot
Reducing Computer Vision Dataset Size via Selective Sampling
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Subset Selection for Autonomous Driving Datasets via Fine-Tuning Vision Foundation Models
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k D-STR: A Method for Spatio-Temporal Data Reduction and Modelling
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Lottery Ticket Search on Untrained Models with Applied Lottery Sample Selection
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kD-STR: A Method for Spatio-Temporal Data Reduction and Modelling
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Modification of a Density-Based Spatial Clustering Algorithm for Applications with Noise for Data Reduction in Intrusion Detection Systems
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