In order to solve the problem of data abnormalities in traditional multi-modal heterogeneous big data detection algorithms and missing data, which leads to data modal confusion, a multi-view heterogeneous big data clustering algorithm based on Kmeans clustering is established. With heterogeneous data as the supporting background of big data, data analysis with the help of multiple views, determination of similarity detection metrics, and a multi-view heterogeneous system based on Kmeans. Then, use the BP neural network to predict the missing attribute values, complete the missing data, restore the big data structure in a heterogeneous state, and then use the BP neural network to denoise the abnormal data to achieve multi-view heterogeneity Research on big data detection algorithms. Both theoretical verification and experimental results show that the accuracy of the proposed method is much higher than that of the original algorithm.

中文翻译：

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基于改进Kmeans算法的多模式异构大数据聚类算法研究

为了解决传统的多模式异构大数据检测算法中数据异常和数据丢失导致数据模态混乱的问题，建立了一种基于Kmeans聚类的多视图异构大数据聚类算法。以异构数据作为大数据的支持背景，借助多视图进行数据分析，确定相似性检测指标，以及基于Kmeans的多视图异构系统。然后，使用BP神经网络预测缺失的属性值，完成缺失的数据，将大数据结构恢复为异构状态，然后使用BP神经网络对异常数据进行去噪以实现多视图异质性。数据检测算法。