With the increase in renewable energy penetration, the impact of uncertain factors on the
efficient operation of multi-energy microgrids (MEMGs) is becoming more and more …

At present, the DBSCAN clustering algorithm has been commonly used principally due to its
ability in discovering clusters with arbitrary shapes. When the cluster number K is …

Soil attributes and their environmental drivers exhibit different patterns in different
geographical directions, along with distinct regional characteristics, which may have …

Clustering, an unsupervised pattern classification method, plays an important role in
identifying input dataset structures. It partitions input datasets into clusters or groups where …

Sudden-onset disasters put forward new requirements for on the state authorities' ability to
analyze public opinion sentiment. However, traditional sentiment analysis methods ignore …

The k-means, one of the most widely used clustering algorithm, is not only faster in
computation but also produces comparatively better clusters. However, it has two major …

The ongoing global coronavirus pandemic (COVID-19) has significantly increased urban
medical waste. Such waste often contains pathogenic microorganisms, harmful chemicals …

Following the philosophy of the block Kaczmarz methods, we propose a randomized block
Kaczmarz method with the blocks determined by the k-means clustering (RBK (k)). It can be …

The k-means algorithm, known for its simplicity and adaptability, faces challenges related to
manual cluster number selection and sensitivity to initial centroid placement. This paper …

Clustering is one of the critical parts of machine learning algorithms. K-Means clustering is
the standard technique that various data analysts use for clustering the data among the …