文章基本信息

标题：An Efficient Algorithm for Mining Frequent Sequential Patterns and Emerging Patterns with Various Constraints
本地全文：下载
作者：C K Bhensdadia ; Y P Kosta
期刊名称：International Journal of Soft Computing & Engineering
电子版ISSN：2231-2307
出版年度：2012
卷号：1
期号：6
页码：59-65
出版社：International Journal of Soft Computing & Engineering
摘要：In many cases, sequential pattern mining still faces tough challenges in both effectiveness and efficiency. On the one hand, there could be a large number of sequential patterns in a large database. A user is often interested in only a small subset of such patterns. Presenting the complete set of sequential patterns may make the mining result hard to understand and hard to use. On the other hand, although efficient algorithms have been proposed, mining a large amount of sequential patterns from large data sequence databases is very expensive task. If we can focus on only those sequential patterns interesting to users, we may be able to save a lot of computation cost by those uninteresting patterns. Many types of constraints can be pushed in sequential pattern mining like item constraint, aggregate constraint, length constraint, gap constraint, duration to enhance the performance.
关键词：Sequential Pattern; Constraints.