Efficient Frequent Chronicle Mining Algorithms: Application to Sleep Disorder
Sequential pattern mining is a dynamic and thriving research field that aims to extract recurring sequences of events from complex datasets. Traditionally, focusing solely on the order of events often falls short of providing precise insights. Consequently, incorporating the temporal intervals betwe...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10411901/ |
| _version_ | 1827365892682416128 |
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| author | Hareth Zmezm Jose Maria Luna Eduardo Almeda Sebastian Ventura |
| author_facet | Hareth Zmezm Jose Maria Luna Eduardo Almeda Sebastian Ventura |
| author_sort | Hareth Zmezm |
| collection | DOAJ |
| description | Sequential pattern mining is a dynamic and thriving research field that aims to extract recurring sequences of events from complex datasets. Traditionally, focusing solely on the order of events often falls short of providing precise insights. Consequently, incorporating the temporal intervals between events has emerged as a vital necessity across various domains, e.g. medicine. Analyzing temporal event sequences within patients’ clinical histories, drug prescriptions, and monitoring alarms exemplifies this critical need. This paper presents innovative and efficient methodologies for mining frequent chronicles from temporal data. The mined graphs offer a significantly more expressive representation than mere event sequences, capturing intricate details of a series of events in a factual manner. The experimental stage includes a series of analyses of diverse databases with distinct characteristics. The proposed approaches were also applied to real-world data comprising information about subjects suffering from sleep disorders. Alluring frequent complete event graphs were obtained on patients who were under the effect of sleep medication. |
| first_indexed | 2024-03-08T08:39:13Z |
| format | Article |
| id | doaj.art-584417b1c4d04da5a22ecb0edce7de47 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-08T08:39:13Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-584417b1c4d04da5a22ecb0edce7de472024-02-02T00:04:27ZengIEEEIEEE Access2169-35362024-01-0112145801459510.1109/ACCESS.2024.335713910411901Efficient Frequent Chronicle Mining Algorithms: Application to Sleep DisorderHareth Zmezm0https://orcid.org/0000-0002-2871-7164Jose Maria Luna1https://orcid.org/0000-0003-3537-2931Eduardo Almeda2Sebastian Ventura3https://orcid.org/0000-0003-4216-6378Department of Computer Science and Numerical Analysis, Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Córdoba, Córdoba, SpainDepartment of Computer Science and Numerical Analysis, Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Córdoba, Córdoba, SpainDepartment of Computer Science and Numerical Analysis, Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Córdoba, Córdoba, SpainDepartment of Computer Science and Numerical Analysis, Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Córdoba, Córdoba, SpainSequential pattern mining is a dynamic and thriving research field that aims to extract recurring sequences of events from complex datasets. Traditionally, focusing solely on the order of events often falls short of providing precise insights. Consequently, incorporating the temporal intervals between events has emerged as a vital necessity across various domains, e.g. medicine. Analyzing temporal event sequences within patients’ clinical histories, drug prescriptions, and monitoring alarms exemplifies this critical need. This paper presents innovative and efficient methodologies for mining frequent chronicles from temporal data. The mined graphs offer a significantly more expressive representation than mere event sequences, capturing intricate details of a series of events in a factual manner. The experimental stage includes a series of analyses of diverse databases with distinct characteristics. The proposed approaches were also applied to real-world data comprising information about subjects suffering from sleep disorders. Alluring frequent complete event graphs were obtained on patients who were under the effect of sleep medication.https://ieeexplore.ieee.org/document/10411901/Frequent event graphschronicle miningsequence miningtemporal data miningsleep disorder |
| spellingShingle | Hareth Zmezm Jose Maria Luna Eduardo Almeda Sebastian Ventura Efficient Frequent Chronicle Mining Algorithms: Application to Sleep Disorder IEEE Access Frequent event graphs chronicle mining sequence mining temporal data mining sleep disorder |
| title | Efficient Frequent Chronicle Mining Algorithms: Application to Sleep Disorder |
| title_full | Efficient Frequent Chronicle Mining Algorithms: Application to Sleep Disorder |
| title_fullStr | Efficient Frequent Chronicle Mining Algorithms: Application to Sleep Disorder |
| title_full_unstemmed | Efficient Frequent Chronicle Mining Algorithms: Application to Sleep Disorder |
| title_short | Efficient Frequent Chronicle Mining Algorithms: Application to Sleep Disorder |
| title_sort | efficient frequent chronicle mining algorithms application to sleep disorder |
| topic | Frequent event graphs chronicle mining sequence mining temporal data mining sleep disorder |
| url | https://ieeexplore.ieee.org/document/10411901/ |
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