Traditional clustering algorithms such as K-means are highly sensitive to the initial centroid
selection and perform poorly on non-convex dataset. To address these problems, a novel …

Data mining's primary clustering method has several uses, including gene analysis. A set of
unlabeled data is divided into clusters using data features in a clustering study, which is an …

To address the instability and performance issues of the classical K-Means algorithm when
dealing with massive datasets, we propose SOSK-Means, an improved K-Means algorithm …

Nonnegative matrix factorization (NMF) for image clustering attains impressive machine
learning performances. However, the current iterative methods for optimizing NMF problems …

The Internet era has brought new challenges and opportunities for English learning and
English teaching. At the same time, basic education is fully implementing quality education …

Statistical modeling plays a critical role in various scientific fields as it offers an
understanding of how the response variable of interest is linked to a range of explanatory …

This paper investigates the process of selecting a hyperparameter for use in a kernel
semiparametric regression model for fusion data, which is an important tool in various …

The density‐based applied spatial clustering algorithm is an algorithm based on high‐
density interconnected regions, which discovers class clusters of arbitrary shapes in noisy …

DBSCAN algorithm is a representative density-based clustering algorithm that has gained
widespread application due to its ability to discover cluster of arbitrarily shapes and …

Conventional parallelized clustering methods for high-dimensional electric power multilevel
data resources are based on a single data feature, which cannot meet the demand of …