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模糊C均值用于聚类分析

于 2022-02-06 发布 文件大小:1.80 kB
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应用背景 模糊c-均值聚类算法 fuzzy c-means algorithm (FCMA)或称( FCM)。在众多模糊聚类算法中,模糊C-均值( FCM) 算法应用最广泛且较成功,它通过优化目标函数得到每个样本点对所有类中心的隶属度,从而决定样本点的类属以达到自动对样本数据进行分类的目的。 关键技术 模糊聚类分析作为无监督机器学习的主要技术之一,是用模糊理论对重要数据分析和建模的方法,建立了样本类属的不确定性描述,能比较客观地反映现实世界,它已经有效地应用在大规模数据分析、数据挖掘、矢量量化、图像分割、模式识别等领域,具有重要的理论与实际应用价值,随着应用的深入发展,模糊聚类算法的研究不断丰富。

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