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Cited by 484

The DOI matches its Crossref record; 65 of the 65 references it cites were found in a registry or an index.

Representation of Structure in Similarity Data: Problems and Prospects

Shepard, Roger N.·1974·Psychometrika
DOI 10.1007/bf02291665
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Abstract

Conclusion: After struggling with the problem of representing structure in similarity data for over 20 years, I find that a number of challenging problems still remain to be overcome—even in the simplest case of the analysis of a single symmetric matrix of similarity estimates. At the same time, I am more optimistic than ever that efforts directed toward surmounting the remaining difficulties will reap both methodological and substantive benefits. The methodological benefits that I forsee include both an improved efficiency and a deeper understanding of “discovery” methods of data analysis. And the substantive benefits should follow, through the greater leverage that such methods will provide for the study of complex empirical phenomena—perhaps particularly those characteristic of the human mind.

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Metadata

Authors
Shepard, R. N.
Venue
Psychometrika
Publisher
Cambridge University Press (CUP)
Published
1974-12
Volume
39
Issue
4
Pages
373-421
ISSN
  • 0033-3123 (ISSN-L)
  • 1860-0980
DOI
10.1007/bf02291665
OpenAlex
W2052167135
Cited by
484
References
65
FWCI
22.34
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Representation of Structure in Similarity Data: Problems and Prospects. https://app.cochat.ai/papers/10.1007/bf02291665
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Shepard, R. N. (1974). Representation of Structure in Similarity Data: Problems and Prospects. Psychometrika. https://doi.org/10.1007/bf02291665

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.