Clustering is a ubiquitous task in data science. Compared to the commonly used k-means
clustering, k-medoids clustering requires the cluster centers to be actual data points and …

Web request query strings (queries), which pass parameters to a referenced resource, are
always manipulated by attackers to retrieve sensitive data and even take full control of victim …

Clustering is a fundamental task in data science with wide-ranging applications. In $ k $-
medoids clustering, cluster centers must be actual datapoints and arbitrary distance metrics …

Facility location problems form a broad class of optimization problems extremely popular in
combinatorial optimization and operations research. In every facility location problem, one …

Most meta-learning methods assume that the (very small) context set used to establish a
new task at test time is passively provided. In some settings, however, it is feasible to actively …

The ProbCover method of Yehuda et al. is a well-motivated algorithm for active learning in
low-budget regimes, which attempts to" cover" the data distribution with balls of a given …

The era of huge data necessitates highly efficient machine learning algorithms. Many
common machine learning algorithms, however, rely on computationally intensive …

The intersectionality framework allows for the combination of formerly additive individual
characteristics into intersectional profiles of employees in order to prioritize and direct further …

Minerals exploration is becoming more difficult, particularly because most mineral deposits
at the surface of the earth have been found. While there may be a lot of sensing data, there …

为了解决在复杂的, 数据量庞大的高光谱图像中汇集出参考价值较高的聚类组合问题,
本文提出一种基于流形的K_Medoids 改进算法并应用于高光谱图像的聚类实践中 …