Contrasting Sequence Groups by Emerging Sequences

Abstract. Group comparison per se is a fundamental task in many scientific endeavours but is also the basis of any classifier. Contrast sets and emerging patterns contrast between groups of categorical data. Comparing groups of sequence data is a relevant task in many applications. We define Emerging Sequences (ESs) as subsequences that are frequent in sequences of one group and less frequent in the sequences of another, and thus distinguishing or contrasting sequences of different classes. There are two challenges to distinguish sequence classes: the extraction of ESs is not trivially efficient and only exact matches of sequences are considered. In our work we address those problems by a suffix tree-based framework and a sliding window matching mechanism for the distance metric between sequences. We propose a classifier for sequence data based on Emerging Sequences. Evaluating against two learning algorithms based on frequent subsequences and exact matching subsequences, the expe...

• Type: Conference
• Year: 2009
• Where: DIS
• Authors: Kang Deng, Osmar R. Zaïane
• Keywords: DIS 2009, Emerging, Sequence Data, Sequences, Theoretical Computer Science