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Record W2118001196 · doi:10.1145/2464576.2464653

Flat vs. symbiotic evolutionary subspace clusterings

2013· article· en· W2118001196 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Clustering Algorithms Research
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of CanadaMitacs
KeywordsSubspace topologyCluster analysisCluster (spacecraft)Computer scienceRepresentation (politics)Space (punctuation)Property (philosophy)Artificial intelligencePattern recognition (psychology)MathematicsData mining

Abstract

fetched live from OpenAlex

Subspace clustering coevolves the attribute space supporting clusters at the same time as parameterizing the cluster location and combination. Typically, a 'flat' representation is pursued in which individuals describe both the property of individual clusters as well as the combination of clusters used to define the overall solution; hereafter F-ESC. Conversely, a symbiotic approach was recently proposed in which candidate clusters and the combination of clusters are coevolved from independent populations; hereafter S-ESC. In this work a common framework is pursued in order for flat and symbiotic evolutionary subspace clustering to be compared directly. We show that F-ESC might match S-ESC results for data sets with high proportions of cluster support, however, the gap between the two algorithm increases as cluster support decreases.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.956
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.003

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 head0.011
GPT teacher head0.250
Teacher spread0.238 · 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

Quick stats

Citations0
Published2013
Admission routes2
Has abstractyes

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