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 …

Mining frequent patterns is a fundamental and crucial task in data-mining problems. The
algorithms reported in the literature for mining frequent patterns can be classified into two …

Online personalised" my*" services are gaining popularity due to a growing customer need
for information filtering and customisation. However, current systems mostly rely on some …

Rare events analysis is an area that includes methods for the detection and prediction of
events, eg a network intrusion or an engine failure, that occur infrequently and have some …

The use of closed-set algorithms to generate condensed accurate representations of a
dataset's frequent itemsets has been well documented. This paper presents a novel …

In stream data mining it is important to use the most recent data to cope with the evolving
nature of the underlying patterns. Simply keeping the most recent records offers no flexibility …

Data mining has been pervasively used for extracting business intelligence to support
business decisionmaking processes. One of the most fundamental and important tasks of …

Aplicações nas áreas do comercio electrónico, telecomunicações, varejo, acções, bio
informática, entre outras, possuem, quase que de forma natural, uma organização …

Devising an efficient one-pass frequent pattern mining algorithm has been an issue in data
mining research in recent past. Pattern growth algorithms like FP-Growth which are found …