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

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