Data Mining is all about data analysis techniques. It is useful for extracting hidden and
interesting patterns from large datasets. Clustering techniques are important when it comes …

The monograph Data Clustering: Theory, Algorithms, and Applications was published in
2007. Starting with the common ground and knowledge for data clustering, the monograph …

As a research branch of data mining, clustering, as an unsupervised learning scheme,
focuses on assigning objects in the dataset into several groups, called clusters, without any …

Today, maritime transportation represents a substantial portion of international trade.
Sustainable development of marine transportation requires systematic modeling and …

In data stream clustering, it is desirable to have algorithms that are able to detect clusters of
arbitrary shape, clusters that evolve over time, and clusters with noise. Existing stream data …

The DBSCAN method for spatial clustering has received significant attention due to its
applicability in a variety of data analysis tasks. There are fast sequential algorithms for …

Clustering is a typical data mining technique that partitions a dataset into multiple subsets of
similar objects according to similarity metrics. In particular, density-based algorithms can find …

DBSCAN is a classic clustering method for identifying clusters of different shapes and isolate
noisy patterns. Despite these qualities, many articles in the literature address the scalability …

DBSCAN is a well-known algorithm for density-based clustering because it can identify the
groups of arbitrary shapes and deal with noisy datasets. However, with the increasing …

The DBSCAN algorithm is a traditional density-based clustering method. This algorithm
allows to identify clusters of different shapes, with the ability to manage noisy patterns in the …