Classification of PavCA Index scores with the k-Means algorithm where k = 3.
<p>Classification of PavCA Index scores with the k-Means algorithm where k = 3.</p>
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2025
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| _version_ | 1852019896123129856 |
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| author | Camille Godin (21445879) |
| author2 | Frédéric Huppé-Gourgues (819818) |
| author2_role | author |
| author_facet | Camille Godin (21445879) Frédéric Huppé-Gourgues (819818) |
| author_role | author |
| dc.creator.none.fl_str_mv | Camille Godin (21445879) Frédéric Huppé-Gourgues (819818) |
| dc.date.none.fl_str_mv | 2025-05-29T17:37:52Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0323893.t001 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/Classification_of_PavCA_Index_scores_with_the_k-Means_algorithm_where_k_3_/29187472 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Ecology Science Policy Infectious Diseases Biological Sciences not elsewhere classified subjective cutoff values researcher &# 8217 relatively small samples reflects unique distributions accommodating different models research often relies provide matlab code explored two approaches pavlovian conditioning studies pavlovian conditioning approach standardized classification framework derivative method based cutoff values used classifying subjects using beyond </ p st ), goal pavca index scores derivative method approaches provide quantified using often arbitrary mean scores gt ), categorize subjects various types specific needs results suggest reduce objectivity means classifier means classification introduce inconsistencies index score inconsistencies stem final days facilitate implementation environmental factors effective tools broader applicability behavioral data behavior classification |
| dc.title.none.fl_str_mv | Classification of PavCA Index scores with the k-Means algorithm where k = 3. |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | <p>Classification of PavCA Index scores with the k-Means algorithm where k = 3.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_847e8ef851e23f513d4b19082d9fb756 |
| identifier_str_mv | 10.1371/journal.pone.0323893.t001 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/29187472 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Classification of PavCA Index scores with the k-Means algorithm where k = 3.Camille Godin (21445879)Frédéric Huppé-Gourgues (819818)EcologyScience PolicyInfectious DiseasesBiological Sciences not elsewhere classifiedsubjective cutoff valuesresearcher &# 8217relatively small samplesreflects unique distributionsaccommodating different modelsresearch often reliesprovide matlab codeexplored two approachespavlovian conditioning studiespavlovian conditioning approachstandardized classification frameworkderivative method basedcutoff values usedclassifying subjects usingbeyond </ pst ), goalpavca index scoresderivative methodapproaches providequantified usingoften arbitrarymean scoresgt ),categorize subjectsvarious typesspecific needsresults suggestreduce objectivitymeans classifiermeans classificationintroduce inconsistenciesindex scoreinconsistencies stemfinal daysfacilitate implementationenvironmental factorseffective toolsbroader applicabilitybehavioral databehavior classification<p>Classification of PavCA Index scores with the k-Means algorithm where k = 3.</p>2025-05-29T17:37:52ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.1371/journal.pone.0323893.t001https://figshare.com/articles/dataset/Classification_of_PavCA_Index_scores_with_the_k-Means_algorithm_where_k_3_/29187472CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/291874722025-05-29T17:37:52Z |
| spellingShingle | Classification of PavCA Index scores with the k-Means algorithm where k = 3. Camille Godin (21445879) Ecology Science Policy Infectious Diseases Biological Sciences not elsewhere classified subjective cutoff values researcher &# 8217 relatively small samples reflects unique distributions accommodating different models research often relies provide matlab code explored two approaches pavlovian conditioning studies pavlovian conditioning approach standardized classification framework derivative method based cutoff values used classifying subjects using beyond </ p st ), goal pavca index scores derivative method approaches provide quantified using often arbitrary mean scores gt ), categorize subjects various types specific needs results suggest reduce objectivity means classifier means classification introduce inconsistencies index score inconsistencies stem final days facilitate implementation environmental factors effective tools broader applicability behavioral data behavior classification |
| status_str | publishedVersion |
| title | Classification of PavCA Index scores with the k-Means algorithm where k = 3. |
| title_full | Classification of PavCA Index scores with the k-Means algorithm where k = 3. |
| title_fullStr | Classification of PavCA Index scores with the k-Means algorithm where k = 3. |
| title_full_unstemmed | Classification of PavCA Index scores with the k-Means algorithm where k = 3. |
| title_short | Classification of PavCA Index scores with the k-Means algorithm where k = 3. |
| title_sort | Classification of PavCA Index scores with the k-Means algorithm where k = 3. |
| topic | Ecology Science Policy Infectious Diseases Biological Sciences not elsewhere classified subjective cutoff values researcher &# 8217 relatively small samples reflects unique distributions accommodating different models research often relies provide matlab code explored two approaches pavlovian conditioning studies pavlovian conditioning approach standardized classification framework derivative method based cutoff values used classifying subjects using beyond </ p st ), goal pavca index scores derivative method approaches provide quantified using often arbitrary mean scores gt ), categorize subjects various types specific needs results suggest reduce objectivity means classifier means classification introduce inconsistencies index score inconsistencies stem final days facilitate implementation environmental factors effective tools broader applicability behavioral data behavior classification |