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COMPUTER ARCHITECTURE FOR IDENTIFYING DATA CLUSTERS USING UNSUPERVISED MACHINE LEARNING IN A CORRELITHM OBJECT PROCESSING SYSTEM
COMPUTER ARCHITECTURE FOR IDENTIFYING DATA CLUSTERS USING UNSUPERVISED MACHINE LEARNING IN A CORRELITHM OBJECT PROCESSING SYSTEM
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机译:在关联对象处理系统中使用无监督的机器学习来识别数据集群的计算机体系结构
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摘要
A device that includes a model training engine implemented by a processor. The model training engine is configured to obtain a set of data values associated with a feature vector. The model training engine is further configured to generate a set of gradients by dividing separation distances by an average separation distance and to compare each gradient to a gradient threshold value. The model training engine is further configured to identify a boundary in response to determining a gradient exceeds the gradient threshold value, to determine a number of identified boundaries, and to determine a number of clusters based on the number of identified boundaries. The model training engine is further configured to train the machine learning model to associate the determined number of clusters with the feature vector.
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