A promising challenge of data mining, especially for association rules technique, is the incremen... more A promising challenge of data mining, especially for association rules technique, is the incremental mining of association whatever is the trend of the association rules. Recently, some researches were devoted to incremental update of temporal association rules problem. In this paper, we focus on cyclic association rules, a class of temporal association rules. Thus, we introduce a new approach called IUPCAR dedicated to maintaining incrementally the cyclic association rules already extracted. Based on the carried out experimental study, we point out the efficiency of our proposal.
A promising challenge of data mining, especially for association rules technique, is the incremen... more A promising challenge of data mining, especially for association rules technique, is the incremental mining of association whatever is the trend of the association rules. Recently, some researches were devoted to incremental update of temporal association rules problem. In this paper, we focus on cyclic association rules, a class of temporal association rules. Thus, we introduce a new approach called IUPCAR dedicated to maintaining incrementally the cyclic association rules already extracted. Based on the carried out experimental study, we point out the efficiency of our proposal.
The cyclicity search in temporal databases aims at discovering regularly repeated rules over time... more The cyclicity search in temporal databases aims at discovering regularly repeated rules over time. Despite the increasing number of the proposed works handling mining cyclic association rules, few works paid attention to generating rules like ("A company producing a given product will sell it with respect to such sales amount. This fact is repeated each month"). In this paper, we propose a new definition of cyclic association rules extracted from several dimensions. Thus, we introduce a new algorithm RACYM for generating such rules. Carried out experiments, conducted on a real data warehouse, show the usefulness and the performance of our proposal.
Never before a such abundant volume of data is collected as the one which we attend nowadays. Thu... more Never before a such abundant volume of data is collected as the one which we attend nowadays. Thus, its exploration becomes increasingly difficult, especially if we highlight the temporal aspect during the extraction of association rules. Therefore, several works were devoted to this problematic by introducing the temporal association rules mining. In this paper, we focus on cyclic association rules, classified as a category of the temporal association rules. Indeed, this class aims to discover new relationships between items that display regular cyclic variation over time. As a response to the anomalies characterizing the classical approaches addressing this issue, i.e., SEQUENTIAL and INTERLEAVED algorithms, we introduce in this paper, a new algorithm called PCAR ALGORITHM. In fact, the major advantages characterizing our approach consist on its performance and its incremental aspect. The experiments were carried out to prove the robustness and the efficiency of our proposed algorithm vs the pioneering approaches in the same trend.
A promising challenge of data mining, especially for association rules technique, is the incremen... more A promising challenge of data mining, especially for association rules technique, is the incremental mining of association whatever is the trend of the association rules. Recently, some researches were devoted to incremental update of temporal association rules problem. In this paper, we focus on cyclic association rules, a class of temporal association rules. Thus, we introduce a new approach called IUPCAR dedicated to maintaining incrementally the cyclic association rules already extracted. Based on the carried out experimental study, we point out the efficiency of our proposal.
A promising challenge of data mining, especially for association rules technique, is the incremen... more A promising challenge of data mining, especially for association rules technique, is the incremental mining of association whatever is the trend of the association rules. Recently, some researches were devoted to incremental update of temporal association rules problem. In this paper, we focus on cyclic association rules, a class of temporal association rules. Thus, we introduce a new approach called IUPCAR dedicated to maintaining incrementally the cyclic association rules already extracted. Based on the carried out experimental study, we point out the efficiency of our proposal.
The cyclicity search in temporal databases aims at discovering regularly repeated rules over time... more The cyclicity search in temporal databases aims at discovering regularly repeated rules over time. Despite the increasing number of the proposed works handling mining cyclic association rules, few works paid attention to generating rules like ("A company producing a given product will sell it with respect to such sales amount. This fact is repeated each month"). In this paper, we propose a new definition of cyclic association rules extracted from several dimensions. Thus, we introduce a new algorithm RACYM for generating such rules. Carried out experiments, conducted on a real data warehouse, show the usefulness and the performance of our proposal.
Never before a such abundant volume of data is collected as the one which we attend nowadays. Thu... more Never before a such abundant volume of data is collected as the one which we attend nowadays. Thus, its exploration becomes increasingly difficult, especially if we highlight the temporal aspect during the extraction of association rules. Therefore, several works were devoted to this problematic by introducing the temporal association rules mining. In this paper, we focus on cyclic association rules, classified as a category of the temporal association rules. Indeed, this class aims to discover new relationships between items that display regular cyclic variation over time. As a response to the anomalies characterizing the classical approaches addressing this issue, i.e., SEQUENTIAL and INTERLEAVED algorithms, we introduce in this paper, a new algorithm called PCAR ALGORITHM. In fact, the major advantages characterizing our approach consist on its performance and its incremental aspect. The experiments were carried out to prove the robustness and the efficiency of our proposed algorithm vs the pioneering approaches in the same trend.
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Papers by Eya BEN AHMED