K-means is a well-known unsupervised-learning algorithm. It assigns data points to k
clusters, the centers of which are termed centroids. However, these centroids have a …

The recent advances in communication and mobile technologies made it easier to access
and share information for most people worldwide. Among the most powerful information …

This paper proposes an online data stream classification that learns with limited labels using
selective self-training. Data partitioning steps are proposed to improve stream mining …

Data stream mining techniques are able to classify evolving data streams such as network
traffic in the presence of concept drift. In order to classify high bandwidth network traffic in …

Conventional network traffic detection methods based on data mining could not efficiently
handle high throughput traffic with concept drift. Data stream mining techniques are able to …

Secure data communication across networks is always threatened with intrusion and abuse.
Network Intrusion Detection System (IDS) is a valuable tool for in-depth defense of computer …

Many incoming data chunks are being produced each day continuously at high speed with
soaring dimensionality, and in most cases, these chunks are unlabeled. Our study combines …

Stream processing frameworks are designed to process the streaming data that arrives in
time. An example of such data is stream of emails that a user receives every day. Most of the …

Compared with the research of data stream with concept drift in supervised environment, the
work that in semi-supervised environment is more challenging. There is currently very little …

Data StreamMining is become new emerging topic for research in knowledge discovery. In
this continuous changing nature of data creates problem in mining the knowledge from it …