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 …

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 …

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 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 …

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 …

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 …

We present an incremental graph-based clustering algorithm whose design was motivated
by a need to extract and retain meaningful information from data streams produced by …

In this paper we propose a data stream clustering algorithm, called Self Organizing density
based clustering over data Stream (SOStream). This algorithm has several novel features …

OPTICS is a density-based clustering method that can address point sets with different
densities; however, the outlier detection capability of OPTICS is limited by several factors …