Estimating the Parameters of Randomly Interleaved Markov Models
2009 (English)Conference paper (Refereed)
Sequences that can be assumed to have been generated by a number of Markov models, whose outputs are randomly interleaved but where the actual sources are hidden, occur in a number of practical situations where data is captured as an unlabeled stream of events. We present a practical method for estimating model parameters on large data sets under the assumption that all sources are identical. Results on representative examples are presented, together with a discussion on the accuracy and performance of the proposed estimation algorithms. Finally, we describe a real-world case study where we apply the technique to the sequence of events recorded in the technical support database of an IT vendor.
Place, publisher, year, edition, pages
Engineering and Technology
IdentifiersURN: urn:nbn:se:kth:diva-144602DOI: 10.1109/ICDMW.2009.17ISI: 000290247100049ScopusID: 2-s2.0-77951187103OAI: oai:DiVA.org:kth-144602DiVA: diva2:714331
IEEE International Conference on Data Mining Workshops (ICDMW)
QC 201405092014-04-272014-04-272014-05-09Bibliographically approved