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Record W2152021196 · doi:10.1109/34.895981

Validity measures for the fuzzy cluster analysis of orientations

2000· article· en· W2152021196 on OpenAlexaff
R.E. Hammah, J.H. Curran

Bibliographic record

VenueIEEE Transactions on Pattern Analysis and Machine Intelligence · 2000
Typearticle
Languageen
FieldComputer Science
TopicData Management and Algorithms
Canadian institutionsRocscience (Canada)
Fundersnot available
KeywordsFuzzy setCluster analysisFuzzy logicArtificial intelligenceComputer scienceFuzzy clusteringData miningIdentification (biology)Discontinuity (linguistics)Pattern recognition (psychology)Mathematics

Abstract

fetched live from OpenAlex

Fuzzy K-means clustering can be applied to the automatic identification of sets in discontinuity data after suitable adaptation of the algorithm. To establish the number of clusters in a data set, modified versions of the validity measures of Gath and Geva (1989), Xie-Beni (1991) and Fukuyama-Sugeno are presented in this paper.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.037
metaresearch head score (Gemma)0.171
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.037
Threshold uncertainty score0.196

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0370.171
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0170.008
Science and technology studies0.0030.008
Scholarly communication0.0050.008
Open science0.0040.007
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.040
GPT teacher head0.294
Teacher spread0.255 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations65
Published2000
Admission routes1
Has abstractyes

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