On the public citation graph
Parallelization of a graph‐cut based algorithm for hierarchical clustering of web documents
0 signed · 21 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.
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known work and none has been retracted — and get a dated CiteStamped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.
What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/(sici)1097-4571(199009)41:6<391::aid-asi1>3.0.co;2-9openalex-snapshot
Indexing by latent semantic analysis
inferredcites →https://doi.org/10.1002/spe.987openalex-snapshot
Parallelization of a dynamic SVD clustering algorithm and its application in information retrieval
inferredcites →https://doi.org/10.1006/jagm.2000.1136openalex-snapshot
Cut Tree Algorithms: An Experimental Study
inferredcites →https://doi.org/10.1007/978-3-540-24655-8_8openalex-snapshot
Semantic, Hierarchical, Online Clustering of Web Search Results
inferredcites →https://doi.org/10.1007/978-3-540-72903-7_20openalex-snapshot
Assessing the Performance of a Graph-Based Clustering Algorithm
inferredcites →https://doi.org/10.1016/b978-0-12-804452-0.09998-xopenalex-snapshot
Introduction to linear algebra
inferredcites →https://doi.org/10.1016/j.datak.2006.10.006openalex-snapshot
A new algorithm for clustering search results
inferredcites →https://doi.org/10.1016/j.eswa.2006.06.009openalex-snapshot
Hierarchically SVM classification based on support vector clustering method and its application to document categorization
inferredcites →https://doi.org/10.1016/j.ins.2011.04.013openalex-snapshot
Minimum spanning tree based split-and-merge: A hierarchical clustering method
inferredcites →https://doi.org/10.1016/j.ipm.2009.09.009openalex-snapshot
Mining fuzzy frequent itemsets for hierarchical document clustering
inferredcites →https://doi.org/10.1016/j.knosys.2011.06.012openalex-snapshot
A quality driven Hierarchical Data Divisive Soft Clustering for information retrieval
inferredcites →https://doi.org/10.1016/j.patrec.2009.11.011openalex-snapshot
Dynamic hierarchical algorithms for document clustering
inferredcites →https://doi.org/10.1016/s0306-4379(01)00008-4openalex-snapshot
Cure: an efficient clustering algorithm for large databases
inferredcites →https://doi.org/10.1080/15427951.2004.10129093openalex-snapshot
Graph Clustering and Minimum Cut Trees
inferredcites →https://doi.org/10.1093/oso/9780195078817.001.0001openalex-snapshot
The Design and Analysis of Parallel Algorithms
inferredcites →https://doi.org/10.2172/5698openalex-snapshot
New Term Weighting Formulas for the Vector Space Method in Information Retrieval
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.2139/ssrn.4894880openalex-extracted
Some Quality Concerns in the Big Data and Machine Learning Software Industry
inferred← citeshttps://doi.org/10.1002/cpe.5094openalex-snapshot
Design and evaluation of a parallel document clustering algorithm based on hierarchical latent semantic analysis
inferred← citeshttps://doi.org/10.1002/cpe.6121openalex-snapshot
A scalable parallel algorithm for building web directories
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.