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MOGA-based fuzzy data mining with taxonomy

机译：基于MOGA的分类学模糊数据挖掘

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摘要

Transactions in real-world applications usually consist of quantitative values. Some fuzzy data mining approaches have thus been proposed for deriving linguistic rules from such transactions. Since membership functions may have a critical influence on the final mining results, several genetic-fuzzy mining approaches have been proposed for mining appropriate membership functions and fuzzy association rules at the same time. Most of them, however, focus on a single level and consider only one objective function. This paper proposes a multi-objective multi-level genetic-fuzzy mining (MOMLGFM) algorithm for mining a set of non-dominated membership functions for mining multi-level fuzzy association rules. The algorithm first encodes the membership functions of each item class (category) into a chromosome according to the given taxonomy. Two objective functions are then considered. The first one is the knowledge amount mined out at different levels, and the second one is the suitability of membership functions. The fitness value of each individual is then evaluated using these two objective functions. After the evolutionary process terminates, various sets of membership functions can be used for deriving multi-level fuzzy association rules according to decision-makers. Experimental results on the simulated and real datasets show the effectiveness of the proposed algorithm.
机译：实际应用中的交易通常由定量值组成。因此，已经提出了一些模糊数据挖掘方法来从这种交易中导出语言规则。由于隶属函数可能对最终的挖掘结果产生关键影响，因此提出了几种遗传-模糊挖掘方法来同时挖掘适当的隶属函数和模糊关联规则。但是，其中大多数只集中在一个级别上，仅考虑一个目标功能。提出了一种多目标多层次遗传模糊挖掘算法，用于挖掘一组非主导隶属度函数，用于挖掘多层次模糊关联规则。该算法首先根据给定的分类法将每个项目类别（类别）的隶属度函数编码为一条染色体。然后考虑两个目标函数。第一个是在不同级别上挖掘出的知识量，第二个是成员函数的适用性。然后使用这两个目标函数评估每个人的适应度值。演化过程终止后，可以根据决策者使用各种隶属函数集来推导多级模糊关联规则。在模拟和真实数据集上的实验结果证明了该算法的有效性。

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  • 来源
    《Knowledge-Based Systems》 |2013年第12期|53-65|共13页
  • 作者

    Chun-Hao Chen; Ji-Syuan He; Tzung-Pei Hong;

  • 作者单位

    Department of Computer Science and Information Engineering, Tamkang University, Taipei 251, Taiwan;

    Department of Computer Science and Information Engineering, Tamkang University, Taipei 251, Taiwan;

    Department of Computer Science and Information Engineering, National University of Kaohsiung. Kaohsiung 811, Taiwan,Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung 804, Taiwan;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Data mining; Fuzzy sets; Fuzzy rules; Multi-objective genetic algorithm; Taxonomy;

    机译：数据挖掘;模糊集;模糊规则;多目标遗传算法;分类;

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