Soft-MSM is a smooth, gradient-enabled version of the context-aware MSM distance for time series alignment that outperforms Soft-DTW alternatives in clustering and nearest-centroid classification.
Breunig, Hans-Peter Kriegel, and Joerg Sander
6 Pith papers cite this work. Polarity classification is still indexing.
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GSHAC performs exact HAC on large geographic point sets by building a sparse geodesic graph and proving that connected-component subproblems yield identical results to the dense algorithm for all standard linkages at cut heights below the sparsity threshold.
VarWISE catalog identifies 457,080 high-confidence infrared variables (49.81% new) and an extended set of 1.9 million from NEOWISE photometry via spatial clustering, VARnet detection, and XGBoost classification.
Lumbermark detects clusters by iteratively chopping large limbs off a mutual reachability MST, yielding partitions of user-specified sizes as a robust alternative to HDBSCAN.
Extends unsupervised eye contact detection for mobile scenarios, reporting significant performance gains on two datasets and new attention metrics.
The effectiveness of dimensionality reduction before clustering depends on matching the specific technique and target dimension count to the data geometry and the clustering algorithm used.
citing papers explorer
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Soft-MSM: Differentiable Context-Aware Elastic Alignment for Time Series
Soft-MSM is a smooth, gradient-enabled version of the context-aware MSM distance for time series alignment that outperforms Soft-DTW alternatives in clustering and nearest-centroid classification.
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Scalable Exact Hierarchical Agglomerative Clustering via Sparse Geographic Distance Graphs
GSHAC performs exact HAC on large geographic point sets by building a sparse geodesic graph and proving that connected-component subproblems yield identical results to the dense algorithm for all standard linkages at cut heights below the sparsity threshold.
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VarWISE: Infrared Variability via NEOWISE Single Exposure Photometry
VarWISE catalog identifies 457,080 high-confidence infrared variables (49.81% new) and an extended set of 1.9 million from NEOWISE photometry via spatial clustering, VARnet detection, and XGBoost classification.
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Lumbermark: Resistant Clustering by Chopping Up Mutual Reachability Minimum Spanning Trees
Lumbermark detects clusters by iteratively chopping large limbs off a mutual reachability MST, yielding partitions of user-specified sizes as a robust alternative to HDBSCAN.
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Accurate and Robust Eye Contact Detection During Everyday Mobile Device Interactions
Extends unsupervised eye contact detection for mobile scenarios, reporting significant performance gains on two datasets and new attention metrics.
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Assessing the impact of dimensionality reduction on clustering performance -- a systematic study
The effectiveness of dimensionality reduction before clustering depends on matching the specific technique and target dimension count to the data geometry and the clustering algorithm used.