Abstract Machine learning have been widely adopted in a variety of fields including
engineering, science, and medicine revolutionizing how data is collected, used, and stored …

The pervasive diffusion of Social Networks (SN) produced an unprecedented amount of
heterogeneous data. Thus, traditional approaches quickly became unpractical for real life …

E-commerce is snowballing with advancements in technology, and as a result,
understanding complex transactional data has become increasingly important. To keep …

The incursion of social media in our lives has been much accentuated in the last decade.
This has led to a multiplication of data mining tools aimed at obtaining knowledge from these …

The huge amount of data being handled today in any environment, such as energy,
economics or healthcare, makes data management systems key to extracting information …

High utility pattern mining (HUPM) discovers meaningful patterns by considering features of
items and utility from non-binary data. Data called stream data is continually generated over …

The large amount of data generated every day makes necessary the re-implementation of
new methods capable of handle with massive data efficiently. This is the case of Association …

Finding frequent itemsets in a continuous streaming data is an important data mining task
which is widely used in network monitoring, Internet of Things data analysis and so on. In the …

As big data often contains a significant amount of unstructured, imprecise, and uncertain
data, the fuzzy-rough-set-based attribute reduction is a valuable technique for uncertainty …

The enormous quantity of data handled by building management systems are key to
develop more efficient energy operational systems. However, the inability of current systems …