模糊聚类分类算法的比较

R. Almeida, J. Sousa
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引用次数: 54

摘要

模糊模型的识别是一项非常复杂的任务。通常,现实世界的数据库有大量的特性,必须选择最相关的特性。近年来,针对分类问题提出了一种新的自动特征选择方法来构建紧凑模糊分类模型。该技术使用了经典的模糊c均值算法。然而,其他的模糊聚类算法,如可能性c-means、模糊可能性c-means或可能性模糊c-means也可以用于聚类数据。一个开放的研究课题是什么聚类算法可以用来导出模糊模型的分类。本文通过比较模糊聚类算法在分类问题中的计算效率和准确性来解决这个问题。这些算法在众所周知的数据集中进行了测试:鸢尾植物、葡萄酒、肝炎、乳腺癌,以及一个困难的现实问题:破产预测
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comparison of fuzzy clustering algorithms for classification
The identification of fuzzy models for classification is a very complex task. Often, real world databases have a large number of features and the most relevant ones must be chosen. Recently, a new automatic feature selection for classification problems was proposed to construct compact fuzzy classification models. This technique used the classical fuzzy c-means algorithm. However, other fuzzy clustering algorithms, such as possibilistic c-means, fuzzy possibilistic c-means or possibilistic fuzzy c-means can be used to cluster the data. An open topic of research is what clustering algorithms can be used to derive fuzzy models for classification. This paper addresses this topic, by comparing fuzzy clustering algorithms in terms of computational efficiency and accuracy in classification problems. The algorithms were tested in well-known data sets: iris plant, wine, hepatitis, breast cancer and in a difficult real-world problem: the prediction of bankruptcy
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