Clustering is an important task in mining evolving data streams. Beside the limited memory
and one-pass constraints, the nature of evolving data streams implies the following …

Mining data streams poses great challenges due to the limited memory availability and real-
time query response requirement. Clustering an evolving data stream is especially …

In data stream clustering, it is desirable to have algorithms that are able to detect clusters of
arbitrary shape, clusters that evolve over time, and clusters with noise. Existing stream data …

Over the decade, a number of attempts have been made towards data stream clustering, but
most of the works fall under clustering by example approach. There are a number of …

Clustering data streams has been attracting a lot of research efforts recently. However, this
problem has not received enough consideration when the data streams are generated in a …

Clustering streaming data presents the problem of not having all the data available at one
time. Further, the total size of the data may be larger than will fit in the available memory of a …

While many clustering techniques have been successfully applied to the person name
disambiguation problem, most do not address two main practical issues: allowing …

The need of fuzzy clustering arises in many real-world applications such as clumping the
users based on their web browsing behavior where the behavior of a user can be similar to …

Event detection is a critical task in sensor networks, especially for environmental monitoring
applications. Traditional solutions to event detection are based on analyzing one-shot data …

Clustering algorithms for streaming data sets are gaining importance due to the availability
of large data streams from different sources. Recently a number of streaming algorithms …