Fuzzy regression analysis using fuzzy clustering

2002 Annual Meeting of the North American Fuzzy Information Processing Society Proceedings. NAFIPS-FLINT 2002 (Cat. No. 02TH8622) Pub Date : 2002-08-07 DOI:10.1109/NAFIPS.2002.1018030

M. Sato-Ilic

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引用次数: 2

Abstract

Proposes an estimation method for fuzzy cluster loading using the kernel method. Fuzzy cluster loading was proposed in order to interpret the result of fuzzy clustering by obtaining the relationship between the obtained fuzzy clusters and the variables of the given data. From the structure of the model for fuzzy cluster loading, it is known that the estimate is obtained using the estimate of the weighted regression analysis. We propose a method to obtain the estimate in a higher space then the space in the given data using the idea of the kernel method. The significant properties of this technique are: (1) we use high dimension space to estimate the fuzzy cluster loading, due to this, we can get a better result to extract the data structure; and (2) through the cluster structure of given data, we can extract a clearer structure of the given data. Several numerical examples show the validity of the proposed technique and the efficiency of the use of the cluster structure in the given data.

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采用模糊聚类进行模糊回归分析

提出了一种基于核方法的模糊聚类负荷估计方法。为了解释模糊聚类的结果，提出了模糊聚类加载方法，通过获取得到的模糊聚类与给定数据变量之间的关系来解释模糊聚类的结果。从模糊聚类负荷模型的结构可知，其估计是利用加权回归分析的估计得到的。我们提出了一种利用核方法的思想在给定数据的更高空间中获得估计的方法。该技术的显著特点是:(1)利用高维空间来估计模糊聚类的负载，因此可以得到较好的数据结构提取结果;(2)通过给定数据的聚类结构，我们可以提取出给定数据更清晰的结构。几个数值算例表明了该方法的有效性和在给定数据中使用聚类结构的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

2002 Annual Meeting of the North American Fuzzy Information Processing Society Proceedings. NAFIPS-FLINT 2002 (Cat. No. 02TH8622)

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