On the public citation graph
Hierarchical trees can be perfectly scaled in one dimension
0 signed · 35 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known work and none has been retracted — and get a dated CiteStamped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/10.1007/bf02289565openalex-snapshot
Multidimensional Scaling by Optimizing Goodness of Fit to a Nonmetric Hypothesis
inferredcites →https://doi.org/10.1007/bf02291218openalex-snapshot
The Relation between Hierarchical and Euclidean Models for Psychological Distances
inferredcites →https://doi.org/10.1007/bf02291665openalex-snapshot
Representation of Structure in Similarity Data: Problems and Prospects
inferredcites →https://doi.org/10.1016/b978-0-12-714250-0.50006-1openalex-snapshot
The Relationship between Multidimensional Scaling and Clustering
inferredcites →https://doi.org/10.1080/00438243.1969.9979428openalex-snapshot
Some problems and methods in statistical archaeology
inferredcites →https://doi.org/10.1111/j.2044-8317.1978.tb00571.xopenalex-snapshot
A short note on a method of seriation
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1002/asm.3150050309openalex-snapshot
Combinatorial data analysis: Confirmatory comparisons between sets of matrices
inferred← citeshttps://doi.org/10.1002/ir.417openalex-snapshot
The use of cluster analysis in typological research on community college students
inferred← citeshttps://doi.org/10.1007/978-1-4614-6744-1_2openalex-snapshot
Statistical and Methodological Considerations When Using Cluster Analysis in Neuropsychological Research
inferred← citeshttps://doi.org/10.1007/978-1-4939-0742-7_16openalex-snapshot
Thinking Ultrametrically, Thinking p-Adically
inferred← citeshttps://doi.org/10.1007/978-1-60761-839-3_5openalex-snapshot
Similarity Searching Using 2D Structural Fingerprints
inferred← citeshttps://doi.org/10.1007/978-3-319-17091-6_37openalex-snapshot
Random Projection Towards the Baire Metric for High Dimensional Clustering
inferred← citeshttps://doi.org/10.1007/978-3-642-23166-7_5openalex-snapshot
Hierarchical Clustering for Finding Symmetries and Other Patterns in Massive, High Dimensional Datasets
inferred← citeshttps://doi.org/10.1007/978-3-642-50974-2_17openalex-snapshot
Clustering in Low-Dimensional Space
inferred← citeshttps://doi.org/10.1007/978-3-642-50974-2_38openalex-snapshot
Interactively Displaying Ultrametric and Additive Trees
inferred← citeshttps://doi.org/10.1007/978-3-642-79999-0_2openalex-snapshot
Representation of Statistical Structures, Classification and Prediction Using Multidimensional Scaling
inferred← citeshttps://doi.org/10.1007/978-981-15-2700-5_24openalex-snapshot
Mapping Networks and Trees with Multidimensional Scaling of Proximities
inferred← citeshttps://doi.org/10.1007/bf01530998openalex-snapshot
Potential applications of three-way multidimensional scaling and related techniques to integrate knowledge from multiple experts
inferred← citeshttps://doi.org/10.1007/s00357-009-9037-9openalex-snapshot
The Remarkable Simplicity of Very High Dimensional Data: Application of Model-Based Clustering
inferred← citeshttps://doi.org/10.1007/s11336-009-9126-zopenalex-snapshot
Clustering Qualitative Data Based on Binary Equivalence Relations: Neighborhood Search Heuristics for the Clique Partitioning Problem
inferred← citeshttps://doi.org/10.1007/s12520-020-01142-xopenalex-snapshot
Ceramic investigation: how to perform statistical analyses
inferred← citeshttps://doi.org/10.1016/b0-08-045044-x/00249-2openalex-snapshot
Compound Selection Using Measures of Similarity and Dissimilarity
inferred← citeshttps://doi.org/10.1016/j.compbiomed.2005.09.002openalex-snapshot
Machine learning in bioinformatics: A brief survey and recommendations for practitioners
inferred← citeshttps://doi.org/10.1016/j.endm.2016.11.006openalex-snapshot
Direct Reading Algorithm for Hierarchical Clustering
inferred← citeshttps://doi.org/10.1016/j.jmgm.2009.06.006openalex-snapshot
Clustering files of chemical structures using the Székely–Rizzo generalization of Ward's method
inferred← citeshttps://doi.org/10.1016/j.sapharm.2007.02.001openalex-snapshot
The uses and benefits of cluster analysis in pharmacy research
inferred← citeshttps://doi.org/10.1093/botlinnean/boab027openalex-snapshot
Resolving páramoPoa(Poaceae): morphometric and phylogenetic analysis of the ‘Cucullatacomplex’ of north-west South America
inferred← citeshttps://doi.org/10.1111/rssb.12228openalex-snapshot
Random-projection Ensemble Classification
inferred← citeshttps://doi.org/10.1134/s0081543809020175openalex-snapshot
Symmetry in data mining and analysis: A unifying view based on hierarchy
inferred← citeshttps://doi.org/10.1142/s0219622003000549openalex-snapshot
CLUSTERING CATEGORICAL AND NUMERICAL DATA: A NEW PROCEDURE USING MULTIDIMENSIONAL SCALING
inferred← citeshttps://doi.org/10.48550/arxiv.1704.01871openalex-snapshot
Massive Data Clustering in Moderate Dimensions from the Dual Spaces of Observation and Attribute Data Clouds
inferred← citeshttps://doi.org/10.5936/csbj.201302002openalex-snapshot
FUSING SIMILARITY RANKINGS IN LIGAND-BASED VIRTUAL SCREENING
inferred← citeshttps://doi.org/10.7916/d8dj5nv7openalex-snapshot
Bayesian Multidimensional Scaling Model for Ordinal Preference Data
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.