Most clustering algorithms require setting one or more parameters, which rely on prior knowledge or are constantly adjusted based on external indicators. To address the issues of requiring external ...

examples_distance.dat is one of the supplementary files in "Clustering by fast search and find of density peaks "sample.txt is an example dataset with 4000 instances and each instance has two features ...

Three new apartment complex projects are in various stages near Seventh Street and Forest Avenue in Tempe.

This study proposes a vector quantization (VQ) model as a robust approach for clustering high-dimensional medical data, particularly in breast cancer classification. The model evolves over time to ...

The illusion of choice: How algorithms shape your online experience One of the key challenges of algorithmic power is opacity - the “black box” nature of AI systems. While these systems collect vast ...

Shape data are found in several applications, such as the analysis of animal anatomy, team formations in American football, and ball kinematics in baseball. Although these data have been better ...

AI shapes how we interact, shop, and think—sometimes in ways we don’t notice. By understanding its role and impact, we can engage with AI intentionally rather than being unconsciously ...

Six clustering algorithms—K-means, hierarchical, affinity propagation, self-organizing map (SOM), fuzzy C-means, and Gustafson-Kessel—were applied to determine the optimal number of clusters, which ...