There are two kinds of sequential rules. They are classical sequential rules and common sequential rules. The common sequential rules present the relationship between unordered itemsets in which all the items in the antecedent part have to appear before the ones in the consequent part. All existing algorithms for mining common sequential rules can not apply to quantitative sequence databases. Furthermore, the common sequential rules found so far did not yet reveal the time gap about the apperance of itemsets in its antecedent and consequent parts. The purpose of this article is to overcome the two disadvantages mentioned above. Specifically, the article proposes an algorithm called IFERMiner to discover common sequential rules in quantitative sequence databases, where the time gap about appearance of two attribute sets in its antecedent and consequent parts is taken account. This algorithm was developed from the ERMiner algorithm that is the most efficient algorithm to discover common sequential rules in transactional sequence databases now. The computational complexity of the IFERMiner algorithm is also shown in the article and it is polynomial. The FCSI rules found out by the IFERMiner algorithm are useful for marketing domain and market analysis.

中文翻译：

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在定量序列数据库中挖掘具有模糊时间间隔的模糊公共序列规则

有两种顺序规则。它们是经典的顺序规则和常见的顺序规则。常见的顺序规则表示无序项集之间的关系，其中前件部分中的所有项目都必须出现在后件部分中的项目之前。所有现有的用于挖掘公共序列规则的算法都不能适用于定量序列数据库。此外，迄今为止发现的常见顺序规则还没有揭示项目集在其前件和后件中出现的时间差距。本文的目的是克服上述两个缺点。具体来说，文章提出了一种名为 IFERMiner 的算法，用于发现定量序列数据库中常见的序列规则，其中考虑了两个属性集在其前件和后件中出现的时间间隔。该算法是从 ERMiner 算法发展而来的，ERMiner 算法是目前在事务序列数据库中发现常见顺序规则的最有效算法。IFERMiner算法的计算复杂度在文章中也有展示，它是多项式的。IFERMiner 算法找出的 FCSI 规则对营销领域和市场分析很有用。