Many applications have been producing streaming data nowadays, which motivates
techniques to extract knowledge from such sources. In this sense, the development of data …

In recent years, several clustering algorithms have been proposed with the aim of mining
knowledge from streams of data generated at a high speed by a variety of hardware …

The solution spaces of many real-world optimization problems change over time. Such
problems are called dynamic optimization problems (DOPs), which pose unique challenges …

We study a problem of batch distribution drift motivated by several applications, which
consists of determining an accurate predictor for a target time segment, for which a moderate …

As many real data sets (eg, social, financial, and medical data sets) are successively
generated in evolution with the ever-changing environment, classification for data stream …

Triclustering algorithms group sets of coordinates of 3-dimensional datasets. In this paper, a
new triclustering approach for data streams is introduced. It follows a streaming scheme of …

Background The amount of data and behavior changes in society happens at a swift pace in
this interconnected world. Consequently, machine learning algorithms lose accuracy …

Many real-world data stream applications not only suffer from concept drift but also class
imbalance. Yet, very few existing studies investigated this joint challenge. Data difficulty …

Handling concept drift in process mining is one of the challenges tasks to construct the
process model. Process model discovery, as the crucial perspective of process mining …

Clustering in dynamic environments is of increasing importance, with broad applications
ranging from real-time data analysis and online unsupervised learning to dynamic facility …