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Cluster analysis is a statistical procedure for grouping observations using an observation-centered approach compared to variable targeted approaches (eg PCA, factor analysis). Whether a pre -processing step for predictive modeling or primary analysis is crucial for determining generalizability between data sets. Theodoridis and Koutroumbas (2008) identified three broad types of validation for cluster analysis: 1) Internal clusterfalidation, 2) Relative clusterfalidation and 3) External clusterfalidation. Strategies for steps 1 and 2 are well established, but cluster analysis is usually a non -controlled learning method where there is no observed result. Ullman et al (2021) proposed an approach to validate a cluster solution by visually inspecting the cluster solutions in a data set for training and validation alternation. This conversation introduces the Clav R package that implements and expands this approach by generating several random samples (using a simple random split or bootstrap monsters). Visualizations of both the cluster profiles and the distributions of the cluster tools are provided together with a shiny application to help the researcher.
For more information about the project, go to: https://github.com/jbryer/clav
A student will also present AI-generated text detection in the context of domain and prompt-specific essays
The widespread acceptance of large language models has made a distinction between people generated by people and AI more challenging. This study investigates AI detection methods for domain and prompt-specific essays in the diagnostic assessment and the reaching of college skills (DAACs) framework, with both random forest and refined Modernbert classifications applying. Our approach contains pre-chatgpt essays, probably generated by humans, in addition to synthetic data sets of essays generated and changed by AI. The random forest classificator was trained with open-source inbeds such as Minilm, Roberta and a cheap OpenAi model, using a one-versus one strategy. The Modernbert method used a new fine-tuning strategy with two levels, using classifications at essay level and marker that combines global text characteristics with detailed phrases through coherenties score and style consistency detection. Together these methods determine effective whether essays have been changed by AI. Our approach offers a cost-effective solution for specific domains and serves as a robust alternative to generic AI detection tools, while it all makes local implementation possible for hardware of consumer quality.
To register for the conference, go to https://ww2.amstat.org/meetings/jsm/2025/
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#Clav #package #shiny #application #validation #cluster #analysis #RBloggers