  Contrast pattern mining (CPM) is an important and popular subfield of data
mining. Traditional sequential patterns cannot describe the contrast
information between different classes of data, while contrast patterns
involving the concept of contrast can describe the significant differences
between datasets under different contrast conditions. Based on the number of
papers published in this field, we find that researchers' interest in CPM is
still active. Since CPM has many research questions and research methods. It is
difficult for new researchers in the field to understand the general situation
of the field in a short period of time. Therefore, the purpose of this article
is to provide an up-to-date comprehensive and structured overview of the
research direction of contrast pattern mining. First, we present an in-depth
understanding of CPM, including basic concepts, types, mining strategies, and
metrics for assessing discriminative ability. Then we classify CPM methods
according to their characteristics into boundary-based algorithms, tree-based
algorithms, evolutionary fuzzy system-based algorithms, decision tree-based
algorithms, and other algorithms. In addition, we list the classical algorithms
of these methods and discuss their advantages and disadvantages. Advanced
topics in CPM are presented. Finally, we conclude our survey with a discussion
of the challenges and opportunities in this field.
