Contrast mining is one of fairly new hot data mining topics. Contrast mining focuses on knowledge that describes differences between classes and conditions, or describes changes over time. Contrast mining aims at developing techniques to discover patterns or models that contrast, and characterize multiple datasets associated with different classes or conditions. Contrast mining has wide applications in reality, due to its ability of simplifying problems and classifying accurately. Research on the mining and application of emerging patterns represents a major direction of contrast mining. This paper provided a survey of such issue. More specifically, after introducing the background, basic concepts and principles of emerging patterns, the paper analyzed the mining methods of emerging patterns, discussed extended definitions of emerging patterns and their mining, stated methods for constructing emerging pattern based classifiers, and illustrated applications of emerging pattern in several real-world fields. Finally, this paper gave out some topics for future research on emerging pattern based contrast mining.