Data mining: concepts and techniques
Data mining: concepts and techniques
Mining minimal distinguishing subsequence patterns with gap constraints
Knowledge and Information Systems
Plant Protein Localization Using Discriminative and Frequent Partition-Based Subsequences
ICDMW '08 Proceedings of the 2008 IEEE International Conference on Data Mining Workshops
Classification of software behaviors for failure detection: a discriminative pattern mining approach
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Contrasting Sequence Groups by Emerging Sequences
DS '09 Proceedings of the 12th International Conference on Discovery Science
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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.