PCA and K-means clustering applied to Raman and PL imaging reveal structural defects in silicon wafers, enhancing understanding of optoelectronic performance.

Background Patients with heart failure (HF) frequently suffer from undetected declines in cardiorespiratory fitness (CRF), which significantly increases their risk of poor outcomes. However, current ...

The popular short form video app has a new corporate structure in the United States, which could result in some changes for the 200 million Americans who use TikTok. By Emmett Lindner TikTok has new ...

Dr. James McCaffrey presents a complete end-to-end demonstration of anomaly detection using k-means data clustering, implemented with JavaScript. Compared to other anomaly detection techniques, ...

Abstract: For radar signal sorting based on pulse descriptors, the inherent limitations of the traditional K-means algorithm include the requirement of a predefined number of clusters, the sensitivity ...

I think this work might be interesting to the scikit-community. In this work, we discuss 2 classical algorithms for an sampling-based version of k-means, which return an epsilon-approximation of the ...

Amsterdam’s struggles with its welfare fraud algorithm show us the stakes of deploying AI in situations that directly affect human lives. What Amsterdam’s welfare fraud algorithm taught me about fair ...

Clustering is an unsupervised machine learning technique used to organize unlabeled data into groups based on similarity. This paper applies the K-means and Fuzzy C-means clustering algorithms to a ...

Abstract: The K-means is sensitive to the initial choice of cluster centers, leading to the results to be different every time. To address this, a new K-means variant based on decision values is ...

CJ Blossom Park, CJ BIO Research Institute, 55, Gwanggyo-ro 42beon-gil, Yeongtong-gu, Suwon-Si, Gyeonggi-do 16495, Republic of Korea ...