Clustering is an essential tool in data mining research and applications. It is the subject of
active research in many fields of study, such as computer science, data science, statistics …

Cluster analysis is an essential tool in data mining. Several clustering algorithms have been
proposed and implemented, most of which are able to find good quality clustering results …

The validation of the results obtained by clustering algorithms is a fundamental part of the
clustering process. The most used approaches for cluster validation are based on internal …

The load data in smart grid contains a lot of valuable knowledge, which is useful for both
electricity producers and consumers. Load classification is an important issue in load data …

Data clustering involves identifying latent similarities within a dataset and organizing them
into clusters or groups. The outcomes of various clustering algorithms differ as they are …

Clustering algorithms have been successfully applied in analyzing building energy
consumption data. It has proven to be an effective technique to identify representative …

As an unsupervised learning method, clustering does not need to know prior knowledge of
the datasets in advance. How determining the optimal number of clusters becomes an …

We use dynamic time warping, a non-parametric pattern recognition method, to study
interlinkages between major energy and agricultural commodity prices. Cluster analysis is …

As the 2019 novel coronavirus disease (COVID-19) pandemic rages globally, its impact has
been felt in the stock markets around the world. Amidst the gloomy economic outlook, certain …

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