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 …

The FP-growth algorithm using the FP-tree has been widely studied for frequent pattern
mining because it can dramatically improve performance compared to the candidate …

In classical association rules mining, a minimum support threshold is assumed to be
available for mining frequent itemsets. However, setting such a threshold is typically hard …

Constraint-based mining of itemsets for questions such as" find all frequent itemsets where
the total price is at least $50" has received much attention recently. Two classes of …

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

The advent of high-throughput technologies such as Illumina HiSeq X, mass spectrometry,
and microarray heralds a new era of big biological datasets in computational biology. This …