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Adaptive clustering and feature selection for categorical time series using interpretable frequency-domain features

Statistics and Its Interface(2023)10.4310/22-sii755Source: DataRank Database

Adaptive clustering and feature selection for categorical time series using interpretable frequency-domain features is a research paper published in Statistics and Its Interface (2023). On theSindex it has a DataRank of 0. It has been cited 1 time.

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0DataRank · unranked

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Open Access1 citations

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datarank_citation_only_1hop_v6· scope data_onlyMethodology

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Enrichment:Funding (1 grants)Impact (FWCI 0.13)OA: greenTopics (26)IDs (PubMed, PMC, OpenAlex)

FAIR Checklist

Context only (not used in score)

Findable (1/2)

Has DOI

Accessible (1/2)

Open Access

Interoperable (0/2)

Reusable (0/3)

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Authors (1)

Scott A. Bruce ORCID

Live enrichment

PubMed:37193362 PMC:PMC10181850 OpenAlex:https://openalex.org/W4365799601

NIGMS NIH HHS

Grant: R01 GM140476

FWCI

0.13

Citation Percentile

0.4%

Citation Trend

2025

green

repositoryhttps://pmc.ncbi.nlm.nih.gov/articles/PMC10181850/pdf/nihms-1845995.pdf

repositoryhttps://www.ncbi.nlm.nih.gov/pmc/articles/10181850

journalhttps://doi.org/10.4310/22-sii755

repositoryhttps://pubmed.ncbi.nlm.nih.gov/37193362

Fields of Study

Time Series Analysis and ForecastingComplex Systems and Time Series AnalysisAnomaly Detection Techniques and Applications

Keywords

Cluster analysisCategorical variablePattern recognition (psychology)Fuzzy clusteringArtificial intelligenceSeries (stratigraphy)Feature selectionData miningComputer scienceFeature (linguistics)MathematicsConsistency (knowledge bases)Machine learningUnsupervised LearningOptimal ScalingSpectral EnvelopePartitional Clustering62H30Categorical Time SeriesPrimary 62m1062m15Secondary 62H86Multiple Time Series