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 …

Contrast set mining is one of the most important tasks in the supervised descriptive pattern
mining field. It aims at finding patterns whose frequencies differ significantly among sets of …

Nowadays the amount of networks of devices and sensors, such as smart homes or smart
cities, is rapidly increasing. Each of these devices generates massive amounts of data on a …

Aspect-based sentiment analysis enables the extraction of fine-grained information, as it
connects specific aspects that appear in reviews with a polarity. Although we detect that the …

Supervised descriptive rule discovery represents a set of data mining techniques whose
objective is to describe data with respect to a property of interest. This concept encompasses …

Discovering patterns in data that best describe the differences between classes allows to
hypothesize and reason about class-specific mechanisms. In molecular biology, for …

Abstract Business Process Deviance Mining is a research area that aims to characterize
deviations of a business process from its expected outcomes. Techniques within this area …

Pattern mining is an essential part of knowledge discovery and data analytics. It is a
powerful paradigm, especially when combined with constraint reasoning. In this overview …

There is a growing interest in the development of classifiers based on contrast patterns
(CPs); partly due to the advantage of them being able to explain classification results in a …

Emerging pattern mining is a data mining task that belongs to the supervized descriptive rule
discovery framework. Its objective is to find rules that describe emerging behavior or …