Association rule mining (ARM) is a data mining technique to discover interesting
associations between datasets. The frequent pattern-growth (FP-growth) is an effective ARM …

High utility itemset mining considers the importance of items such as profit and item
quantities in transactions. Recently, mining high utility itemsets has emerged as one of the …

Finding frequent patterns in a continuous stream of transactions is critical for many
applications such as retail market data analysis, network monitoring, web usage mining, and …

As data have been accumulated more quickly in recent years, corresponding databases
have also become huger, and thus, general frequent pattern mining methods have been …

Regular pattern mining has been emerged as one of the promising sub-domains of data
mining by discovering patterns with regular occurrences throughout a complete database. In …

Outstanding frequent pattern mining guarantees both fast runtime and low memory usage
with respect to various data with different types and sizes. However, it is hard to improve the …

The cyber world provides an anonymous environment for criminals to conduct malicious
activities such as spamming, sending ransom e-mails, and spreading botnet malware. Often …

The rapid development of network technology and its evolution toward heterogeneous
networks has increased the demand to support automatic monitoring and the management …

A novel algorithm for efficiently mining high average-utility itemsets is presented in this
paper. The algorithm utilizes list structures, which compactly capture all information needed …

Pattern mining is a data mining technique used for discovering significant patterns and has
been applied to various applications such as disease analysis in medical databases and …