Over the past decade, we have witnessed an enormous amount of research effort dedicated
to the design of efficient outlier detection techniques while taking into consideration …

Summarization has been proven to be a useful and effective technique supporting data
analysis of large amounts of data. Knowledge discovery from data (KDD) is time consuming …

Abstract Massive Online Analysis (MOA) is a software environment for implementing
algorithms and running experiments for online learning from evolving data streams. MOA is …

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 of high dimensional data streams is an important problem in many application
domains, a prominent example being network monitoring. Several approaches have been …

Due to the ever growing presence of data streams, there has been a considerable amount of
research on stream mining algorithms. While many algorithms have been introduced that …

With the increase of sensor and monitoring applications, data mining on streaming data is
receiving increasing research attention. As data is continuously generated, mining …

The goal of continuous learning is to acquire and fine-tune knowledge incrementally without
erasing already existing knowledge. How to mitigate this erasure, known as catastrophic …

With the proliferation of applications generating vast volumes of data streams, numerous
clustering methods have emerged to process and extract valuable insights from this data …

This paper reviews related work and state-of-the-art publications for recognizing motor
symptoms of Parkinson's Disease (PD). It presents research efforts that were undertaken to …