Emerging pattern mining is a data mining task that aims to discover discriminative patterns,
which can describe emerging behavior with respect to a property of interest. In recent years …

Supervised classification based on Contrast Patterns (CP) is a trending topic in the pattern
recognition literature, partly because it contains an important family of both understandable …

The usage of imbalanced databases is a recurrent problem in real-world data such as
medical diagnostic, fraud detection, and pattern recognition. Nevertheless, in class …

In several problems, contrast pattern-based classifiers produce high accuracy and provide
an explanation of the result in terms of the patterns used for classification. However, class …

Nowadays, most data is generated by devices that produce data continuously. These kinds
of data can be categorized as data streams and valuable insights can be extracted from …

Identifying differences between groups is one of the most important knowledge discovery
problems. The procedure, also known as contrast sets mining, is applied in a wide range of …

The value of a company's Web site depends on visitors performing actions that add value to
the company. Those actions are called conversions. We present techniques for …

Today, the number of existing devices generates immense amounts of data on a continuous
basis that must be processed by new distributed data stream mining approaches. In this …

Risk patterns are crucial in biomedical research and have served as an important factor in
precision health and disease prevention. Despite recent development in parallel and high …

In this paper, a cooperative-competitive multi-objective evolutionary fuzzy system called
E2PAMEA is presented for the extraction of emerging patterns in big data environments …