New Algorithm for Frequent Itemsets Mining from Evidential Data Streams

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• 作者：Amine Farhat ; ^{farhat_amine@yahoo.fr" class="auth_mail" title="E-mail the corresponding author} ; Mohamed Salah Gouider ^{ms.gouider@yahoo.fr" class="auth_mail" title="E-mail the corresponding author} ; Lamjed Ben Said ^{bensaid_lamjed@yahoo.fr" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Frequent Itemsets ; Data Streams ; Data Mining ; Uncertainty ; Evidence Theory ; Belief Functions ; Approximation
• 刊名：Procedia Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：96
• 期：Complete
• 页码：645-653
• 全文大小：244 K

文摘

Mining frequent itemsets is a very interesting issue in Data Streams handling, useful for several real world applications. This task reveals many challenges such the one-pass principle as well as performance problems due to the huge volumes of Data Streams. Performance is defined in terms of CPU and main memory consumption in terms of uncertainty management issues. In this paper, we introduce the concept of Evidential Data Streams and we present a new innovative algorithm for mining frequent itemsets from evidential data streams, based on the evidence theory concepts.