The main purpose of data mining and analytics is to find novel, potentially useful patterns
that can be utilized in real-world applications to derive beneficial knowledge. For identifying …

Frequent itemset mining (FIM) is an essential task within data analysis since it is responsible
for extracting frequently occurring events, patterns, or items in data. Insights from such …

With the growing popularity of shared resources, large volumes of complex data of different
types are collected automatically. Traditional data mining algorithms generally have …

It is widely known that there is a lot of useful information hidden in big data, leading to a new
saying that “data is money.” Thus, it is prevalent for individuals to mine crucial information for …

Mining useful patterns from varied types of databases is an important research topic, which
has many reallife applications. Most studies have considered the frequency as sole …

Mining high utility itemsets is an emerging and very active research area in data mining. The
goal is to mine all itemsets with a utility value, in terms of importance to the user, no less than …

High-utility itemset mining is a popular data mining problem that considers utility factors,
such as quantity and unit profit of items besides frequency measure from the transactional …

High-utility sequential pattern (HUSP) mining is an emerging topic in the field of knowledge
discovery in databases. It consists of discovering subsequences that have a high utility …

Association-rule mining (ARM) or frequent itemset mining (FIM) is the most fundamental task
in knowledge discovery, which is used to find the occurrence frequency of the item/sets in …

Abstract The Internet of Things (IoT) and smart cities are flourishing with distributed systems
in mobile wireless networks. As a result, an enormous amount of data are being generated …