Association rule mining is a technique that is widely used in data mining. This technique is
used to identify interesting relationships between sets of items in a dataset and predict …

Gap constraint sequential pattern mining (SPM), as a kind of repetitive SPM, can avoid
mining too many useless patterns. However, this method is difficult for users to set a suitable …

Mining closed high utility itemsets (CHUIs) involves finding a representative set of HUIs that
is usually smaller than that of HUIs but can generate the full HUIs without loss of information …

In contrast to frequent itemset mining (FIM) algorithms that focus on identifying itemsets with
high occurrence frequency, high-utility itemset mining algorithms can reveal the most …

One of various pattern mining techniques, the High Utility Pattern Mining (HUPM) is a
method for finding meaningful patterns from non-binary databases by considering the …

Most traditional pattern mining is designed to process binary databases, so there is a limit to
extracting meaningful information from real-world databases. To solve this problem, high …

Weighted single large graphs are often used to simulate complex systems, and thus mining
frequent subgraphs in a weighted large graph is an important issue that has attracted the …

With booming e-commerce and World Wide Web (WWW), a powerful tool in customer
relationship management (CRM), called the RFM analysis model, has been used to ensure …

In some situations, finding the rare association rule is of higher importance than the frequent
itemset. Unique rules represent rare cases, activities, or events in real-world applications. It …

The problem of discovering high-utility itemsets (HUIs) in transaction databases, which is an
extension of Frequent Itemset Mining, is a commonly encountered mining task. Researchers …