Abstract Machine learning classifiers perform well on balanced datasets. Unfortunately, a lot
of the real-world data sets are naturally imbalanced. So, imbalanced classification is a …

Density based clustering methods are proposed for clustering spatial databases with noise.
Density Based Spatial Clustering of Applications with Noise (DBSCAN) can discover …

Events are happening in real world and real time, which can be planned and organized for
occasions, such as social gatherings, festival celebrations, influential meetings, or sports …

Leveraging application-level requirements using coflows has recently been shown to
improve application-level communication performance in data-parallel clusters. However …

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 …

In the field of Smart City applications, the analysis of urban data to detect city hotspots, ie,
regions where urban events (such as pollution peaks, virus infections, traffic spikes, and …

Density-based spatial clustering of noisy applications (DBSCAN), a widely used density-
based clustering technique, faces challenges in determining its key parameter, Eps, leading …

Any effective smart city application proposal must consider both the technological and policy
challenges to be optimally beneficial to the city; and not only in functioning of the narrow …

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 …

New and effective approaches for the analysis of seismic data make it possible to identify the
distribution of earthquakes hel** further to assess frequency of occurrence any associated …