Nonoverlapping sequential pattern mining is an important type of sequential pattern mining
(SPM) with gap constraints, which not only can reveal interesting patterns to users but also …

High utility pattern mining is an emerging data science task, which consists of discovering
patterns having a high importance in databases. The utility of a pattern can be measured in …

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 …

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 …

Data mining has been actively studied, and it has become more important due to the
development of information technology and the demands of diverse applications, such as …

Frequent pattern mining extracts most frequent patterns from databases. These frequency-
based frameworks have limitations in representing users' interest in many cases. In business …

Discovering frequent trends in time series is a critical task in data mining. Recently, order-
preserving matching was proposed to find all occurrences of a pattern in a time series …

Closed high utility itemsets (CHUIs) and maximal high utility itemsets (MaxHUIs) are two
important concise representations of HUIs. Discovering these itemsets is important because …

The aim of sequential pattern mining (SPM) is to discover potentially useful information from
a given sequence. Although various SPM methods have been investigated, most of these …

Discovering high average utility itemsets (HAUIs) in a quantitative database is a popular
data mining task, which aims at identifying sets of products (items) purchased together that …