MétaCan
Menu
Back to cohort
Record W2368161388

Mining multivariate association patterns from spatiotemporal data

2011· article· en· W2368161388 on OpenAlexaff
Jianqun Chen

Bibliographic record

VenueJournal of Central South University(Science and Technology) · 2011
Typearticle
Languageen
FieldComputer Science
TopicData Mining Algorithms and Applications
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsMultivariate statisticsAssociation rule learningData miningComputer scienceAssociation (psychology)GraphMultivariate analysisArtificial intelligenceMachine learningTheoretical computer science
DOInot available

Abstract

fetched live from OpenAlex

To mine multivariate association patterns(MVAP),such as star-like and sequence multivariate association patterns,from large spatiotemporal datasets,a combination of multi-class-features(MCF) and multi-association-rules(MAR) was proposed according to entity types and relationships.The MVAP was then established based on the methods of graph theory.A new MVAP algorithm was presented by introducing Equivalence Classes to quickly identify MVAP.By use of an example in urban planning used to explain the MVAP process for spatiotemporal data mining,the results show that the MVAP and their algorithm have applicability.

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)0.003
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0060.007
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.032
GPT teacher head0.218
Teacher spread0.187 · 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 designSimulation or modeling
Domainnot available
GenreEmpirical

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

Citations0
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Central South University(Science and Technology)Same topicData Mining Algorithms and ApplicationsFrench-language works237,207