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首页> 外文期刊>International Journal of Uncertainty, Fuzziness, and Knowledge-based Systems >A FUZZY DATA MINING ALGORITHM FOR FINDING SEQUENTIAL PATTERNS
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A FUZZY DATA MINING ALGORITHM FOR FINDING SEQUENTIAL PATTERNS

机译：查找顺序模式的模糊数据挖掘算法。

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摘要

Since fuzzy knowledge representation can facilitate interaction between an expert system and its users, the effective construction of a fuzzy knowledge base is important. Fuzzy sequential patterns described by natural language are one type of fuzzy knowledge representation, and can thus be helpful in building a prototype fuzzy knowledge base. We define that a fuzzy sequence is an ordered list of frequent fuzzy grids, and the length of a fuzzy sequence is the number of frequent fuzzy grids in the frequent fuzzy sequence. Frequent fuzzy grids and frequent fuzzy sequences can be determined by comparing individual fuzzy supports with the user-specified minimum fuzzy support. A fuzzy sequential pattern is just a frequent fuzzy sequence, but it is not contained in any other frequent fuzzy sequence. In this paper, an effective algorithm called the Fuzzy Grids Based Sequential Patterns Mining Algorithm (FGBSPMA) is proposed to generate fuzzy sequential patterns. A numerical example is used to show an analysis of the user visit to websites, demonstrating the usefulness of the proposed algorithm.
机译：由于模糊知识表示可以促进专家系统及其用户之间的交互，因此有效构建模糊知识库非常重要。自然语言描述的模糊顺序模式是模糊知识表示的一种类型，因此可以帮助建立原型模糊知识库。我们定义模糊序列是频繁模糊网格的有序列表，模糊序列的长度是频繁模糊序列中频繁模糊网格的数量。通过将各个模糊支持与用户指定的最小模糊支持进行比较，可以确定频繁的模糊网格和频繁的模糊序列。模糊顺序模式只是一个频繁的模糊序列，但不包含在任何其他频繁的模糊序列中。本文提出了一种有效的算法，即基于模糊网格的序列模式挖掘算法（FGBSPMA）来生成模糊序列模式。一个数字示例用于显示用户访问网站的分析，证明了所提出算法的有用性。

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