An Occurrence Based Approach to Mine Emerging Sequences

An important purpose of sequence analysis is to find the distinguishing characteristics of sequence classes. Emerging Sequences (ESs), subsequences that are frequent in sequences of one group and less frequent in the sequences of another, can contrast sequences of different classes and thus facilitating sequence classification. Different approaches have been developed to extract ESs, in which various mining criterions are applied. In our work we compare Emerging Sequences fulfilling different constraints. By measuring ESs with their occurrences, introducing gap constraint and keeping the uniqueness of items, our ESs demonstrate desirable discriminative power. Evaluating against two mining algorithms based on support and no gap constraint subsequences, the experiments on two types of datasets show that the ESs fulfilling our selection criterions achieve a satisfactory classification accuracy: an average F-measure of 93.2% is attained when the experiments are performed on 11 datasets.