Introducing Concept of Fuzzy Support Matrix for Interestingness Measures

Swati Ramdasi
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Abstract

Fuzzy association rules with its linguistic annotations and human interpretable form, has provided a convenient extension of association concepts to quantified attributes. The applicability is extended by combining extraction of both positive and negative association rules. Interestingness measures are used to filter out the useful and correct set of actionable association rules from the larger set of rules mined by association rule mining algorithms. Many measures such as Support, Confidence, Conviction and Certainty Factor, with their own area of applicability and statistical significance are popular. The wide range of measures is usually based on frequency counts or probability of occurrence of certain attribute patterns. Binary attributes uses a 2×2 contingency table as the basis for defining different measures. This paper presents concept of fuzzy support matrix using fuzzy partitions, as a natural extension of contingency table for the different interestingness measures. Those can be defined in a uniform and consistent manner. It uses the existing interestingness measures defined in new form using fuzzy support and illustrate these concepts using known data sets. This paper represent active research directions aimed at advancing the capabilities, applicability, and efficiency of fuzzy association rule mining in handling modern data challenges across various domains. Keywords: Interestingness measures; Association Rules mining; Fuzzy sets.
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引入趣味性测量的模糊支持矩阵概念
模糊关联规则具有语言注释和人类可解释的形式,为将关联概念扩展到量化属性提供了便利。通过将正关联规则和负关联规则的提取结合起来,扩展了适用性。兴趣度量用于从关联规则挖掘算法挖掘出的大量规则中筛选出有用且正确的可操作关联规则集。支持度、置信度、确信度和确定性因子等许多衡量标准都有各自的适用范围和统计意义。各种度量通常基于频率计数或某些属性模式出现的概率。二元属性使用 2×2 或然率表作为定义不同度量的基础。本文提出了使用模糊分区的模糊支持矩阵概念,作为或然率表的自然扩展,用于不同的趣味性度量。它们可以以统一一致的方式进行定义。本文使用模糊支持以新形式定义了现有的趣味性度量,并使用已知数据集说明了这些概念。本文代表了积极的研究方向,旨在提高模糊关联规则挖掘的能力、适用性和效率,以应对各领域的现代数据挑战。关键词趣味性度量;关联规则挖掘;模糊集。
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