Clustering is a solution for classifying enormous data when there is not any early knowledge
about classes. With emerging new concepts like cloud computing and big data and their vast …

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 …

The significant growth of interconnected Internet‐of‐Things (IoT) devices, the use of social
networks, along with the evolution of technology in different domains, lead to a rise in the …

Data stream mining is an active research area that has recently emerged to discover
knowledge from large amounts of continuously generated data. In this context, several data …

Nowadays, with the advance of technology, many applications generate huge amounts of
data streams at very high speed. Examples include network traffic, web click streams, video …

Clustering validation has long been recognized as one of the vital issues essential to the
success of clustering applications. In general, clustering validation can be categorized into …

Clustering data streams has drawn lots of attention in the last few years due to their ever-
growing presence. Data streams put additional challenges on clustering such as limited time …

As more and more applications produce streaming data, clustering data streams has
become an important technique for data and knowledge engineering. A typical approach is …

Measuring the quality of a clustering algorithm has shown to be as important as the
algorithm itself. It is a crucial part of choosing the clustering algorithm that performs best for …

Clustering, one of the most important unsupervised learning problems, is the task of dividing
a set of objects into clusters such that objects within the same cluster are similar while …