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An Interdisciplinary Comparison of Sequence Modeling Methods for Next-Element Prediction [article]

Niek Tax, Irene Teinemaa, Sebastiaan J. van Zelst
2018 arXiv   pre-print
in the machine learning field methods such as (hidden) Markov models and recurrent neural networks have been developed and successfully applied to a wide-range of tasks, (ii) in process mining process discovery  ...  Indulpet Miner [54] The Indulpet miner is another process-tree-based process discovery algorithm that aims to address the shortcomings of the IM algorithm by combining several existing process tree mining  ...  The Indulpet miner starts with applying the IM algorithm.  ... 
arXiv:1811.00062v1 fatcat:ccng5cyq7zebfkyoeqfobr5ciy

Process discovery using in-database minimum self distance abstractions

Alifah Syamsiyah, Sander J. J. Leemans
2020 Proceedings of the 35th Annual ACM Symposium on Applied Computing  
Evolutionary Tree Miner [3] , Split Miner [2] , Indulpet Miner [14] ) or do not provide basic guarantees such as soundness (e.g.  ...  -Infrequent (IMf) [12] , (2) Indulpet Miner (IN) [14] (our experiment extends the evaluation in [14] ), (3) Evolutionary Tree Miner (ETM) [3] , (4) Split Miner (SM) [2] , and (5) the baseline flower  ... 
doi:10.1145/3341105.3373846 dblp:conf/sac/SyamsiyahL20 fatcat:5ibvoda63na3tmzil7zj5vcn74

Mining Insights from Weakly-Structured Event Data [article]

Niek Tax
2019 arXiv   pre-print
The first category of methods focuses on preprocessing event logs in order to enable process discovery techniques to extract insights into unstructured event data.  ...  . - An approach to detect and filter from event logs so-called chaotic activities, which are activities that cause process discovery methods to overgeneralize. - A supervised approach to abstract low-level  ...  algorithm, called the Indulpet Miner, that brings ideas from the area of ensemble learning to the field of process discovery by combining the results of several other process discovery algorithms.  ... 
arXiv:1909.01421v1 fatcat:wjsv7wif4napbetlm57kplf76u