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Fouille d’épisodes à partir de données incertaines (Episode mining from uncertain data)

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dc.contributor.author Ouarem, Oualid
dc.date.accessioned 2024-06-20T11:24:04Z
dc.date.available 2024-06-20T11:24:04Z
dc.date.issued 2024-06
dc.identifier.issn MD/23
dc.identifier.uri https://dspace.univ-bba.dz:443/xmlui/handle/123456789/5034
dc.description.abstract Data mining is a critical process in the discovery of knowledge from data. Its primary objective is to extract interesting patterns that implicitly indicate significant relationships between items. Different branches of data mining manipulate various types of data. Episode mining is a subfield of data mining that aims to uncover valuable knowledge from temporal data in the form of a single, long sequence of events. The sequence may not always certain data; it may be noisy, sourced from multiple sources, or collected with errors. Consequently, there is a need to develop and design algorithms to extract frequent episodes from uncertain data. This thesis proposes novel algorithms for frequent episode and episode rule mining in the case of certain data and addresses also the challenges associated with these tasks in the context of uncertain en_US
dc.language.iso en en_US
dc.publisher UNIVERSITY BBA en_US
dc.subject Episode mining, episode rules, NONEPI, EMDO, UEMDO, prediction, uncertaindata en_US
dc.title Fouille d’épisodes à partir de données incertaines (Episode mining from uncertain data) en_US
dc.type Thesis en_US


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