Abstract Number of connected devices is steadily increasing and these devices continuously
generate data streams. Real-time processing of data streams is arousing interest despite …

In a dynamic stream there is an assumption that the underlying process generating the
stream is non-stationary and that concepts within the stream will drift and change as the …

Cloud and Fog computing has emerged as a promising paradigm for the Internet of things
(IoT) and cyber–physical systems (CPS). One characteristic of CPS is the reciprocal …

Maternal perinatal depression (PND), a common mental disorder with a prevalence of over
10%, is associated with long‐term health risks for both mothers and offspring. This study …

Analyzing data streams has received considerable attention over the past decades due to
the widespread usage of sensors, social media and other streaming data sources. A core …

Clustering is a key data mining task. This is the problem of partitioning a set of observations
into clusters such that the intra-cluster observations are similar and the inter-cluster …

In recent years, a significant boost in data availability for persistent data streams has been
observed. These data streams are continually evolving, with the clusters frequently forming …

Software organizations are increasingly incorporating machine learning (ML) into their
product offerings, driving a need for new data management tools. Many of these tools …

A data stream classification method called DISSFCM (Dynamic Incremental Semi-
Supervised FCM) is presented, which is based on an incremental semi-supervised fuzzy …

Existing text clustering methods utilize only one representation at a time (single view),
whereas multiple views can represent documents. The multiview multirepresentation …