Data mining can generally be defined as a technique for finding patterns (extraction) or
interesting information in large amounts of data that have meaning for decision support. One …

High average utility pattern mining is the concept proposed to complement drawbacks of
high utility pattern mining by considering lengths of patterns along with the utilities of the …

High utility pattern mining has been proposed to overcome the limitations of frequent pattern
mining which cannot reflect the unique profits of items. High utility pattern mining has been …

Recommender system suggests a personalized recommendation by filtering the information
based on users interest. Nowadays, users like to purchase the best possible items and …

The movement of tourists has important economic and social implications for destination
management. However, tracking and analyzing such movement can be a challenge both …

Erasable pattern mining is one of the important fields of frequent pattern mining. It diagnoses
and solves the economic problems that arise in the manufacturing industry. The real-world …

Traditional association rule mining algorithms can have a long runtime, high memory
consumption, and generate a huge number of rules. Browsing through numerous rules and …

High-utility itemset mining (HUIM) in transaction databases has been extensively studied to
discover interesting itemsets from users' purchase behaviors. With this, business managers …

Early warning on the ship deficiency is crucial for enhancing maritime safety, improving
maritime traffic efficiency, reducing ship fuel consumption, etc. Previous studies focused on …

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 …