Many frequent pattern mining algorithms find patterns from traditional transaction databases,
in which the content of each transaction—namely, items—is definitely known and precise …

As an important data mining and knowledge discovery task, association rule mining
searches for implicit, previously unknown, and potentially useful pieces of information—in …

With advances in technology, a flood of data can be produced in many applications such as
sensor networks and Web click streams. This calls for efficient techniques for extracting …

Since its introduction, frequent-pattern mining has been the subject of numerous studies,
including incremental updating. Many existing incremental mining algorithms are Apriori …

Since its introduction, frequent-pattern mining has been the subject of numerous studies,
including incremental updating. Many existing incremental mining algorithms are Apriori …

Local pattern mining algorithms generate sets of patterns, which are typically not directly
useful and have to be further processed before actual application or interpretation. Rather …

Since its introduction, mining of frequent patterns has been the subject of numerous studies.
Generally, they focus on improving algorithmic efficiency for finding frequent patterns or on …

Nowadays, high volumes of massive data can be generated from various sources (eg,
sensor data from environmental surveillance). Many existing distributed frequent itemset …

Big data are everywhere as high volumes of varieties of valuable precise and uncertain data
can be easily collected or generated at high velocity in various real-life applications …

Many existing data mining algorithms search interesting patterns from transactional
databases of precise data. However, there are situations in which data are uncertain. Items …