Various applications, such as electronic business, satellite remote sensing, intrusion
discovery, and network traffic monitoring, generate large unbounded data stream sequences …

High-utility itemset mining (HUIM) is an important task in data mining that can retrieve more
meaningful and useful patterns for decision-making. One-phase HUIM algorithms based on …

With the growing volume of data, mining Frequent Itemsets remains of paramount
importance. These have applications in various domains such as market basket analysis …

Mining frequent and high utility itemsets (FHUIs) from transaction database is an important
task in data mining. In order to overcome the difficulties of parameter setting and huge …

Stream Clustering algorithms have gained a lot of importance in the recent past due to rapid
rising utilities of IoT systems and applications. Anytime algorithms and frameworks play a …

Effective anomaly detection in telecommunication networks is essential for securing digital
transactions and supporting the sustainability of our global information ecosystem. However …

With the increase of real‐time stream data, knowledge discovery from stream data becomes
more and more important, which requires an efficient data structure to store transactions and …

The streams where multiple transactions are associated with the same key are prevalent in
practice, eg, a customer has multiple shop** records arriving at different time. Itemset …

Support based popular itemset mining often misguides the users in proper knowledge
discovery in connection with many real world applications such as social network analysis …

Efficient regular-frequent pattern mining from sensors-produced data has become a
challenge. The large volume of data leads to prolonged runtime, thus delaying vital …