Drawing Lines in Psychological Space: What K-means Clustering Reveals in Simulated and Real Psychometric Data
Authors:
Pedro Henrique Ramos Pinto,
Maria Jullyanna Ferreira Marques,
Luiz Carlos Serramo Lopez
Abstract:
K-means clustering is widely used in psychological and psychometric research to identify profiles, subgroups, and potential typologies, yet its classical formulation does not test whether such groups exist as latent psychological categories. Instead, K-means partitions multidimensional space into regions around centroids, favoring compact, approximately spherical clusters defined by geometric dist…
▽ More
K-means clustering is widely used in psychological and psychometric research to identify profiles, subgroups, and potential typologies, yet its classical formulation does not test whether such groups exist as latent psychological categories. Instead, K-means partitions multidimensional space into regions around centroids, favoring compact, approximately spherical clusters defined by geometric distance. In this paper, we examine this limitation through a sequence of controlled simulated datasets. We then extend the analysis to the SMARVUS dataset, a large international psychometric dataset comprising survey responses from university students across 35 countries, to evaluate whether similar geometric partitioning patterns emerge in empirical psychological data. By contrasting simulated and empirical data, this paper argues that K-means can produce stable and visually coherent clustering solutions even in continuous Gaussian latent spaces without true subgroup structure.
△ Less
Submitted 7 May, 2026;
originally announced May 2026.
Supercharging Federated Learning with Flower and NVIDIA FLARE
Authors:
Holger R. Roth,
Daniel J. Beutel,
Yan Cheng,
Javier Fernandez Marques,
Heng Pan,
Chester Chen,
Zhihong Zhang,
Yuhong Wen,
Sean Yang,
Isaac,
Yang,
Yuan-Ting Hsieh,
Ziyue Xu,
Daguang Xu,
Nicholas D. Lane,
Andrew Feng
Abstract:
Several open-source systems, such as Flower and NVIDIA FLARE, have been developed in recent years while focusing on different aspects of federated learning (FL). Flower is dedicated to implementing a cohesive approach to FL, analytics, and evaluation. Over time, Flower has cultivated extensive strategies and algorithms tailored for FL application development, fostering a vibrant FL community in re…
▽ More
Several open-source systems, such as Flower and NVIDIA FLARE, have been developed in recent years while focusing on different aspects of federated learning (FL). Flower is dedicated to implementing a cohesive approach to FL, analytics, and evaluation. Over time, Flower has cultivated extensive strategies and algorithms tailored for FL application development, fostering a vibrant FL community in research and industry. Conversely, FLARE has prioritized the creation of an enterprise-ready, resilient runtime environment explicitly designed for FL applications in production environments. In this paper, we describe our initial integration of both frameworks and show how they can work together to supercharge the FL ecosystem as a whole. Through the seamless integration of Flower and FLARE, applications crafted within the Flower framework can effortlessly operate within the FLARE runtime environment without necessitating any modifications. This initial integration streamlines the process, eliminating complexities and ensuring smooth interoperability between the two platforms, thus enhancing the overall efficiency and accessibility of FL applications.
△ Less
Submitted 22 July, 2024; v1 submitted 21 May, 2024;
originally announced July 2024.