后挖掘环境中序列模式的有效周期性挖掘

F. Anwar, I. Petrounias, V. Kodogiannis, V. Tasseva, D. Peneva
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引用次数: 6

摘要

顺序模式挖掘方法主要处理寻找顺序模式的积极行为,这些行为可以帮助预测一系列事件之后的下一个事件。此外,顺序模式也可能表现出周期性,例如,在周末,80%在电影院看完电影的人会在餐馆吃饭。这是一个尚未在文献中研究过的问题。为了解决序列模式的周期性发现问题,我们采用并扩展了一种用于关联规则挖掘的周期模式挖掘方法。然而,由于序列模式的顺序/时间性质,查找给定序列模式的周期性的过程大大增加了上述关联规则挖掘方法的复杂性。作为任何数据挖掘策略的关键属性,我们为上述问题提供了一个全面而灵活的问题定义框架。介绍了两种主要的采矿技术,以促进采矿过程。引入了间隔验证过程(IVP)来中和由于序列模式的时间/顺序性质而出现的复杂性,而设计了进程切换机制(PSM),通过仅扫描源数据库中的相关数据集来提高挖掘过程的效率。本文提出的方法是基于后挖掘环境,其中从数据库中识别顺序模式已经发生。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Efficient periodicity mining of sequential patterns in a post-mining environment
Sequential pattern mining approaches mainly deal with finding the positive behaviour of a sequential pattern that can help in predicting the next event after a sequence of events. In addition, sequential patterns may exhibit periodicity as well, i.e. during weekends 80% of people who watch a movie in cinemas will have a meal in a restaurant afterwards. This is a problem that has not been studied in the literature. To confront the problem of discovering periodicity for sequential patterns we adopt and extend a periodic pattern mining approach which has been utilised in association rule mining. However, due to the sequential/temporal nature of sequential patterns, the process of finding the periodicity of a given sequential pattern increases the complexity of the above mentioned association rule mining approach considerably. As a key attribute of any data mining strategy we provide a comprehensive and flexible problem definition framework for the above mentioned problem. Two main mining techniques are introduced to facilitate the mining process. The Interval Validation Process (IVP) is introduced to neutralise complexities which emerge due to the temporal/sequential nature of sequential patterns, whereas the Process Switching Mechanism (PSM) is devised to increase the efficiency of the mining process by only scanning relevant data-sets from the source database. The approach proposed in this paper is based on a post-mining environment, where the identification of sequential patterns from a database has already taken place.
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