Clustering refers to the task of identifying groups or clusters in a data set. In density‐based
clustering, a cluster is a set of data objects spread in the data space over a contiguous …

Fairness related to locations (ie,“where”) is critical for the use of machine learning in a
variety of societal domains involving spatial datasets (eg, agriculture, disaster response …

A well-trained deep learning (DL) model often cannot achieve expected performance after
deployment due to the mismatch between the distributions of the training data and the field …

Cluster detection is important and widely used in a variety of applications, including public
health, public safety, transportation, and so on. Given a collection of data points, we aim to …

Identifying black spots effectively and accurately is a pivotal and challenging task to improve
road traffic safety. A novel black spot identification model is proposed by integrating the GIS …

HDBSCAN*, a state-of-the-art density-based hierarchical clustering method, produces a
hierarchical organization of clusters in a dataset wrt a parameter mpts. While a small change …

Researchers' interest in Fog Computing and its application in different sectors has been
increasing since the last decade. To discover the emerging trends inherent to this …

Early identification of abnormal flight conditions is extremely critical in terms of safety
precautions and preventing possible incidents. Because there is a massive amount of raw …

We propose an efficient model-based clustering approach for creating Gaussian Mixture
Models from finite datasets. Models are extracted from HDBSCAN* hierarchies using the …