A framework for analyzing single-cell genomics data, in which geometrical properties are harnessed to obtain insights on cellular diversity, including precise clustering, clear visualizations, and ...

The subthalamic nucleus contains subpopulations with different contributions to deliberative decision-making based on noisy evidence and reward-driven preferences.

A privacy-preserving marketing framework applies homomorphic encryption to perform machine learning on encrypted ...

Objective SLE is a heterogeneous systemic autoimmune disease with diverse clinical manifestations. We aimed to identify ...

Abstract: In k-means clustering, we are given a set of n data points in d-dimensional space R/sup d/ and an integer k and the problem is to determine a set of k points in Rd, called centers, so as to ...

Department of Chemistry, University of Florida, Gainesville, Florida 32611, United States Quantum Theory Project, University of Florida, Gainesville, Florida 32611, United States Laufer Center for ...

Abstract: The k-means algorithm is generally the most known and used clustering method. There are various extensions of k-means to be proposed in the literature. Although it is an unsupervised ...

k-means clustering partitions a multi-dimensional data set into k clusters, where each data point belongs to the cluster with the nearest mean, serving as a prototype of the cluster.

A single-cell sequencing data set has always been a challenge for clustering because of its high dimension and multi-noise points. The traditional K-means algorithm is not suitable for this type of ...