In the last two decades, machine learning research and practice has focused on batch
learning, usually with small datasets. Nowadays there are applications in which the data are …

High utility pattern (HUP) mining over data streams has become a challenging research
issue in data mining. When a data stream flows through, the old information may not be …

Extracting frequent patterns from databases has always been an imperative task for the data
mining community. Literature has endowed plentiful endeavors to this research area with …

Weighted frequent pattern (WFP) mining is more practical than frequent pattern mining
because it can consider different semantic significance (weight) of the items. For this reason …

High utility pattern (HUP) mining is one of the most important research issues in data mining.
Although HUP mining extracts important knowledge from databases, it requires long …

Mining frequent itemsets from transactional data streams has become very essential in
today's world with many applications such as stock market analysis, retail chain analysis …

As the volume of digital commerce and communication has exploded, the demand for data
mining of streaming data has likewise grown. One of the fundamental data mining tasks, for …

Gradual rules allow users to be provided with rules describing the ordering correlations
among attributes. Such a rule is for instance given by the higher the salary and the lower the …

In spite of being a small country, concerning geographic area and population size, Portugal
has a very active and respected Artificial Intelligence community, with a good number of …

Mining frequent patterns over data streams is an interesting and challenging problem due to
the emergence of new applications and limited resources of main memory and processing …