Concerning unsatisfactory classification accuracy and low efficiency of the existing pattern-based classification methods for model training, a concept of density-aware pattern and an algorithm for biological sequence classification based on density-aware patterns, namely BSC (Biological Sequence Classifier), were proposed. Firstly, frequent sequence patterns based on density-aware concept were mined. Then, the mined frequent sequence patterns were filtered and sorted for designing the classification rules. Finally, the sequences without classification were classified by classification rules. According to a number of experiments conducted on four real biological sequence datasets, the influence of BSC algorithm parameters on the results were analyzed and the recommended parameter settings were provided. Meanwhile, the experimental results showed that the accuracies of BSC algorithm were improved by at least 2.03 percentage points compared with other four pattern-based baseline algorithms. The results indicate that BSC algorithm has high biological sequence classification accuracy and execution efficiency.

With the development of e-commerce, online shopping Web sites provide reviews for helping a customer to make the best choice. However, the number of reviews is huge, and the content of reviews is typically redundant and non-standard. Thus, it is difficult for users to go through all reviews in a short time and find the distinguishing characteristics of a product from the reviews. To resolve this problem, a method to mine top- k emerging patterns was proposed and applied to mining reviews of different products. Based on the proposed method, a prototype, called ReviewScope, was designed and implemented. ReviewScope can find significant comments of certain goods as decision basis, and provide visualization results. The case study on real world data set of JD.com demonstrates that ReviewScope is effective, flexible and user-friendly.

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.