The purpose of this study is to enhance data quality and overall accuracy and improve certainty by reducing the negative impacts of the FCM algorithm while clustering real-world data and also decreasing the inherent noise in data sets.,The present study proposed a new effective model based on fuzzy C-means (FCM), ensemble filtering (ENS) and machine learning algorithms, called an FCM-ENS model. This model is mainly composed of three parts: noise detection, noise filtering and noise classification.,The performance of the proposed model was tested by conducting experiments on six data sets from the UCI repository. As shown by the obtained results, the proposed noise detection model very effectively detected the class noise and enhanced performance in case the identified class noisy instances were removed.,To the best of the authors’ knowledge, no effort has been made to improve the FCM algorithm in relation to class noise detection issues. Thus, the novelty of existing research is combining the FCM algorithm as a noise detection technique with ENS to reduce the negative effect of inherent noise and increase data quality and accuracy.

中文翻译：

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模糊C均值和集成滤波的协同组合用于类噪声检测

本研究的目的是通过减少 FCM 算法的负面影响，同时对真实世界数据进行聚类并减少数据集中的固有噪声来提高数据质量和整体准确性并提高确定性。本研究提出了一种新的有效模型基于模糊 C 均值 (FCM)、集成滤波 (ENS) 和机器学习算法，称为 FCM-ENS 模型。该模型主要由噪声检测、噪声过滤和噪声分类三部分组成。,通过在UCI存储库的六个数据集上进行实验测试了所提出模型的性能。如所获得的结果所示，所提出的噪声检测模型非常有效地检测了类噪声，并在去除识别出的类噪声实例的情况下提高了性能。，据作者所知，没有努力改进与类噪声检测问题相关的 FCM 算法。因此，现有研究的新颖之处在于将 FCM 算法作为噪声检测技术与 ENS 相结合，以减少固有噪声的负面影响并提高数据质量和准确性。