Classification of PavCA Index scores with the k-Means algorithm where k = 3.

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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Bibliographic Details
Main Author:	Camille Godin (21445879) (author)
Other Authors:	Frédéric Huppé-Gourgues (819818) (author)
Published:	2025
Subjects:	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
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Summary:	<p>Classification of PavCA Index scores with the k-Means algorithm where k = 3.</p>

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