Detection of Sequential Outliers Using a Variable Length Markov Model

Cécile Low-Kam, Anne Laurent, Maguelonne Teisseire
2008 2008 Seventh International Conference on Machine Learning and Applications  
The problem of mining for outliers in sequential datasets is crucial to forward appropriate analysis of data. Therefore, many approaches for the discovery of such anomalies have been proposed. However, most of them use a sample of known typical sequences to build the model. Besides, they remain greedy in terms of memory usage. In this paper we propose an extension of one such approach, based on a Probabilistic Suffix Tree and on a measure of similarity. We add a pruning criterion which reduces
more » ... he size of the tree while improving the model, and a sharp inequality for the concentration of the measure of similarity, to better sort the outliers. We prove the feasability of our approach through a set of experiments over a protein database.
doi:10.1109/icmla.2008.137 dblp:conf/icmla/Low-KamLT08 fatcat:vvazzyjc2ndjxoho3uvt6vboie