Optimal segmentation using tree models

摘要

Sequence data are abundant in application areas such as computational biology, environmental sciences, and telecommunications. Many real-life sequences have a strong segmental structure, with segments of different complexities. In this paper we study the description of sequence segments using variable length Markov chains (VLMCs), also known as tree models. We discover the segment boundaries of a sequence and at the same time we compute a VLMC for each segment. We use the Bayesian information criterion (BIC) and a variant of the minimum description length (MDL) principle that uses the Krichevsky-Trofimov (KT) code length to select the number of segments of a sequence. On DNA data the method selects segments that closely correspond to the annotated regions of the genes.