Discovering high utility itemsets in transaction databases is a key task for studying the
behavior of customers. It consists of finding groups of items bought together that yield a high …

Traditional sequential pattern mining methods were designed for symbolic sequence. As a
collection of measurements in chronological order, a time series needs to be discretized into …

Top-k high utility itemset mining is the process of discovering the k itemsets having the
highest utilities in a transactional database. In recent years, several algorithms have been …

High-utility itemset mining (HUIM) is a popular data mining task with applications in
numerous domains. However, traditional HUIM algorithms often produce a very large set of …

Mining high-utility itemsets (HUIs) is one of the most important research topics in data mining
because HUIs consider non-binary frequency values of items in transactions and different …

Frequent itemsets mining is an active research problem in the domain of data mining and
knowledge discovery. With the advances in database technology and an exponential …

High on-shelf utility itemset (HOU) mining is an emerging data mining task which consists of
discovering sets of items generating a high profit in transaction databases. The task of HOU …

Frequent weighted itemsets (FWIs) are a variation of frequent itemsets (FIs) that take into
account the different importance or weights for each item. Many algorithms have been …

Mining uncertain frequent patterns (UFPs) from uncertain databases was recently
introduced, and there are various approaches to solve this problem in the last decade …

Mining erasable patterns (EPs) is one of the emerging tasks in data mining which helps
factory managers to establish plans for the development of new systems of products …