Title :
Estimating the Parameters of Randomly Interleaved Markov Models
Author :
Gillblad, Daniel ; Steinert, Rebecca ; Ferreira, Diogo R.
Author_Institution :
Swedish Inst. of Comput. Sci., Kista, Sweden
Abstract :
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.
Keywords :
Markov processes; parameter estimation; very large databases; IT vendor; parameter estimation; randomly interleaved Markov models; technical support database; Cloud computing; Clustering algorithms; Computer networks; Costs; Data mining; Data processing; Decision trees; Machine learning algorithms; Parameter estimation; Training data;
Conference_Titel :
Data Mining Workshops, 2009. ICDMW '09. IEEE International Conference on
Conference_Location :
Miami, FL
Print_ISBN :
978-1-4244-5384-9
Electronic_ISBN :
978-0-7695-3902-7
DOI :
10.1109/ICDMW.2009.17