Using interesting sequences to interactively build Hidden Markov Models

Jaroszewicz, Szymon

doi:10.1007/s10618-010-0171-0

Cited by 8 publications

(3 citation statements)

References 24 publications

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“…Finally, Jaroszewicz (2010) describes the use of local patterns in the form of event sequences for the interactive construction of a global Hidden Markov Model. The local pattern discovery focuses on sequences that occur more frequently than it would be expected from the constructed model.…”

Section: The Special Issuementioning

confidence: 99%

Guest Editorial: Global modeling using local patterns

Fürnkranz

Knobbe

2010

Data Min Knowl Disc

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Section: The Special Issuementioning

confidence: 99%

Guest Editorial: Global modeling using local patterns

Fürnkranz

Knobbe

2010

Data Min Knowl Disc

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“…Markov models in all their variants are frequently used to mine patterns in sequential data. Consider, for instance, 1 st order Markov chains (Wilks 1999;Pirolli and Pitkow 1999;Sarukkai 2000), Hidden Markov Models (HMM) (Jaroszewicz 2010;Peharz et al 2014;Meier et al 2015;Bueno et al 2019) and Dynamic Bayesian Networks (DBN) (Dagum et al 1992;Bueno et al 2020). All these models are called memoryless if they satisfy the Markov property: given the data at time t − 1, the data at time t is independent of the data before time t − 1.…”

Section: Introductionmentioning

confidence: 99%

Reactive Water Sorbents for the Sorption-Enhanced Reverse Water–Gas Shift

Pieterse¹,

Elzinga²,

Booneveld³

et al. 2021

Catal Lett

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Section: Introductionmentioning

confidence: 99%

Mining sequences with exceptional transition behaviour of varying order using quality measures based on information-theoretic scoring functions

Schouten

Bueno

Duivesteijn

et al. 2021

Data Min Knowl Disc

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Discrete Markov chains are frequently used to analyse transition behaviour in sequential data. Here, the transition probabilities can be estimated using varying order Markov chains, where order k specifies the length of the sequence history that is used to model these probabilities. Generally, such a model is fitted to the entire dataset, but in practice it is likely that some heterogeneity in the data exists and that some sequences would be better modelled with alternative parameter values, or with a Markov chain of a different order. We use the framework of Exceptional Model Mining (EMM) to discover these exceptionally behaving sequences. In particular, we propose an EMM model class that allows for discovering subgroups with transition behaviour of varying order. To that end, we propose three new quality measures based on information-theoretic scoring functions. Our findings from controlled experiments show that all three quality measures find exceptional transition behaviour of varying order and are reasonably sensitive. The quality measure based on Akaike’s Information Criterion is most robust for the number of observations. We furthermore add to existing work by seeking for subgroups of sequences, as opposite to subgroups of transitions. Since we use sequence-level descriptive attributes, we form subgroups of entire sequences, which is practically relevant in situations where you want to identify the originators of exceptional sequences, such as patients. We show this relevance by analysing sequences of blood glucose values of adult persons with diabetes type 2. In the experiments, we find subgroups of patients based on age and glycated haemoglobin (HbA1c), a measure known to correlate with average blood glucose values. Clinicians and domain experts confirmed the transition behaviour as estimated by the fitted Markov chain models.

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Using interesting sequences to interactively build Hidden Markov Models

Cited by 8 publications

References 24 publications

Guest Editorial: Global modeling using local patterns

Guest Editorial: Global modeling using local patterns

Reactive Water Sorbents for the Sorption-Enhanced Reverse Water–Gas Shift

Mining sequences with exceptional transition behaviour of varying order using quality measures based on information-theoretic scoring functions

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