Online learning represents a family of machine learning methods, where a learner attempts
to tackle some predictive (or any type of decision-making) task by learning from a sequence …

Data growth in today's world is exponential, many applications generate huge amount of
data streams at very high speed such as smart grids, sensor networks, video surveillance …

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 …

The pervasiveness of the smart Internet of Things (IoTs) enables many electric sensors and
devices to be connected and generates a large amount of dataflow. Compared with …

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 …

Most data streams encountered in real life are data objects with mixed numerical and
categorical attributes. Currently most data stream algorithms have shortcomings including …

Density-based method has emerged as a worthwhile class for clustering data streams.
Recently, a number of density-based algorithms have been developed for clustering data …

Multi-view clustering aims at integrating the complementary information between different
views so as to obtain an accurate clustering result. In addition, the traditional clustering is a …

Change in a data stream can occur at the concept level and at the feature level. Change at
the feature level can occur if new, additional features appear in the stream or if the …

Streaming data are potentially infinite sequence of incoming data at very high speed and
may evolve over the time. This causes several challenges in mining large scale high speed …