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 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 …

Sequential pattern mining (SPM) has been applied in many fields. However, traditional SPM
neglects the pattern repetition in sequence. To solve this problem, gap constraint SPM was …

In pattern mining, users generally set a minimum threshold to find useful patterns from
databases. As a result, patterns with higher values than the user-given threshold are …

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 …

Nonoverlapping sequential pattern mining, as a kind of repetitive sequential pattern mining
with gap constraints, can find more valuable patterns. Traditional algorithms focused on …

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 …

The traditional methods for mining association rules (ARs) include two phrases: mining
frequent itemsets (FIs)/frequent closed itemsets (FCIs)/frequent maximal itemsets (FMIs) and …

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 …

Nonoverlapping sequential pattern mining (SPM) is a type of SPM with gap constraints that
can mine valuable information in sequences. One of the disadvantages of nonoverlapping …