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HACAM: Hierarchical Agglomerative Clustering Around Medoids - and its Limitations
Free accessTIBKAT | 2021| -
Stop using the elbow criterion for k-means and how to choose the number of clusters instead
Free accessArXiv | 2022| -
Faster k-Medoids Clustering: Improving the PAM, CLARA, and CLARANS Algorithms
Springer Verlag | 2019| -
Faster k-Medoids Clustering: Improving the PAM, CLARA, and CLARANS Algorithms
Free accessArXiv | 2018| -
Intrinsic t-Stochastic Neighbor Embedding for Visualization and Outlier Detection
A Remedy Against the Curse of Dimensionality?Springer Verlag | 2017| -
Fast and Eager k-Medoids Clustering: O(k) Runtime Improvement of the PAM, CLARA, and CLARANS Algorithms
Free accessArXiv | 2020| -
Clustering by Direct Optimization of the Medoid Silhouette
British Library Conference Proceedings | 2022| -
An Alternating Optimization Scheme for Binary Sketches for Cosine Similarity Search
Springer Verlag | 2023| -
Explicit Formulae to Interchangeably use Hyperplanes and Hyperballs using Inversive Geometry
Free accessArXiv | 2024| -
MESS: Manifold Embedding Motivated Super Sampling
Springer Verlag | 2021|Contributors: Schubert, Erich -
CANDLE: Classification And Noise Detection With Local Embedding Approximations
Free accessTIBKAT | 2021| -
BETULA: Numerically Stable CF-Trees for BIRCH Clustering
British Library Conference Proceedings | 2020| -
ELKI: A large open-source library for data analysis - ELKI Release 0.7.5 "Heidelberg"
Free accessArXiv | 2019| -
Intrinsic t-Stochastic Neighbor Embedding for Visualization and Outlier Detection: A Remedy Against the Curse of Dimensionality?
British Library Conference Proceedings | 2017| -
The (black) art of runtime evaluation: Are we comparing algorithms or implementations?
Springer Verlag | 2016|
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