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 …

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 …

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 …

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 …

As the center of the financial market, the stock market is popular with the public attention of
investors. It is of great significance for investors that an effective analytic method of stock …