On the public citation graph

Coresets for Discrete Integration and Clustering

https://doi.org/10.1007/11944836_6
0 signed · 49 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

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

inferredcites →https://doi.org/10.1007/11944836_6openalex-snapshot
Coresets for Discrete Integration and Clustering
inferredcites →https://doi.org/10.1007/978-3-642-03942-3openalex-snapshot
Geometric Discrepancy: An Illustrated Guide
inferredcites →https://doi.org/10.1016/0304-3975(85)90224-5openalex-snapshot
Clustering to minimize the maximum intercluster distance
inferredcites →https://doi.org/10.1016/j.comgeo.2004.03.003openalex-snapshot
A local search approximation algorithm for k-means clustering
inferredcites →https://doi.org/10.1016/j.comgeo.2006.02.003openalex-snapshot
How to get close to the median shape
inferredcites →https://doi.org/10.1017/9781009701259openalex-snapshot
Combinatorial and Computational Geometry
inferredcites →https://doi.org/10.1017/9781009701259.002openalex-snapshot
Geometric Approximation via Coresets
inferredcites →https://doi.org/10.1109/focs.2006.22openalex-snapshot
Coresets forWeighted Facilities and Their Applications
inferredcites →https://doi.org/10.1109/sfcs.2000.892111openalex-snapshot
Testing of clustering
inferredcites →https://doi.org/10.1109/sfcs.2000.892123openalex-snapshot
Polynomial time approximation schemes for geometric k-clustering
inferredcites →https://doi.org/10.1109/sffcs.1999.814587openalex-snapshot
A sublinear time approximation scheme for clustering in metric spaces
inferredcites →https://doi.org/10.1137/s0895480102410973openalex-snapshot
Testing of Clustering
inferredcites →https://doi.org/10.1145/1007352.1007400openalex-snapshot
On coresets for k-means and k-median clustering
inferredcites →https://doi.org/10.1145/1064092.1064114openalex-snapshot
Smaller coresets for k-median and k-means clustering
inferredcites →https://doi.org/10.1145/1109557.1109687openalex-snapshot
On k -Median clustering in high dimensions
inferredcites →https://doi.org/10.1145/1137856.1137915openalex-snapshot
How to get close to the median shape
inferredcites →https://doi.org/10.1145/177424.178042openalex-snapshot
Applications of weighted Voronoi diagrams and randomization to variance-based k -clustering
inferredcites →https://doi.org/10.1145/513400.513402openalex-snapshot
A local search approximation algorithm for k-means clustering
inferredcites →https://doi.org/10.1145/62212.62255openalex-snapshot
Optimal algorithms for approximate clustering
inferredcites →https://doi.org/10.48550/arxiv.1810.12826openalex-snapshot
Coresets for $k$-Means and $k$-Median Clustering and their Applications
inferredcites →https://doi.org/10.5555/1109557.1109687openalex-snapshot
On k-Median clustering in high dimensions
inferredcites →https://doi.org/10.5555/338219.338603openalex-snapshot
Approximation algorithms for projective clustering
inferredcites →https://doi.org/10.5555/365411.365499openalex-snapshot
Sublinear time approximate clustering
inferredcites →https://doi.org/10.5555/644108.644240openalex-snapshot
Smaller core-sets for balls

What points at this work

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

inferred← citeshttps://doi.org/10.1002/widm.1335openalex-snapshot
Core‐sets: An updated survey
inferred← citeshttps://doi.org/10.1007/11944836openalex-snapshot
FSTTCS 2006 : foundations of software technology and theoretical computer science : 26th International Conference, Kolkata, India, December 13-15, 2006 : proceedings
inferred← citeshttps://doi.org/10.1007/978-3-030-29349-9_2openalex-snapshot
Core-Sets: Updated Survey
inferred← citeshttps://doi.org/10.1007/978-3-642-38756-2_29openalex-snapshot
On the Complexity of Approximate Sum of Sorted List
inferred← citeshttps://doi.org/10.1007/978-981-95-7127-7_8openalex-snapshot
Linear Time Small Coresets for k-Mean Clustering of Segments with Applications
inferred← citeshttps://doi.org/10.1109/focs.2006.22openalex-snapshot
Coresets forWeighted Facilities and Their Applications
inferred← citeshttps://doi.org/10.1109/focs.2012.31openalex-snapshot
Down the Rabbit Hole: Robust Proximity Search and Density Estimation in Sublinear Space
inferred← citeshttps://doi.org/10.1109/iemcon.2016.7746254openalex-snapshot
Disaster forecasting using convex hull & K-median approach
inferred← citeshttps://doi.org/10.1109/ipsn.2012.6920941openalex-snapshot
An effective coreset compression algorithm for large scale sensor networks
inferred← citeshttps://doi.org/10.1137/1.9781611973075.50openalex-snapshot
Universal ε-approximators for integrals
inferred← citeshttps://doi.org/10.1137/1.9781611973099.105openalex-snapshot
A Near-Linear Algorithm for Projective Clustering Integer Points
inferred← citeshttps://doi.org/10.1145/1993636.1993712openalex-snapshot
A unified framework for approximating and clustering data
inferred← citeshttps://doi.org/10.1145/2185677.2185739openalex-snapshot
An effective coreset compression algorithm for large scale sensor networks
inferred← citeshttps://doi.org/10.1145/2424321.2424325openalex-snapshot
The single pixel GPS
inferred← citeshttps://doi.org/10.1145/2814569openalex-snapshot
iDiary
inferred← citeshttps://doi.org/10.1201/9781315119601-48openalex-snapshot
Coresets and Sketches
inferred← citeshttps://doi.org/10.17077/etd.1r1kp6v3openalex-snapshot
Coresets, complexity of shapes, and total sensitivity
inferred← citeshttps://doi.org/10.4230/lipics.icalp.2022.51openalex-snapshot
Introduction to Core-sets: an Updated Survey
inferred← citeshttps://doi.org/10.48550/arxiv.1111.2942openalex-snapshot
Down the Rabbit Hole: Robust Proximity Search and Density Estimation in Sublinear Space
inferred← citeshttps://doi.org/10.48550/arxiv.1501.06783openalex-snapshot
Big Data on the Rise: Testing monotonicity of distributions
inferred← citeshttps://doi.org/10.48550/arxiv.1706.05554openalex-snapshot
Coresets for Vector Summarization with Applications to Network Graphs
inferred← citeshttps://doi.org/10.48550/arxiv.1802.07382openalex-snapshot
Generic Coreset for Scalable Learning of Monotonic Kernels: Logistic Regression, Sigmoid and more
inferred← citeshttps://doi.org/10.5555/1873601.1873651openalex-snapshot
Universal ε-approximators for integrals
inferred← citeshttps://doi.org/10.5555/2095116.2095222openalex-snapshot
Data reduction for weighted and outlier-resistant clustering
showing the first 24 of 25 · page through all 25
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.