COMPUTER ARCHITECTURE FOR IDENTIFYING DATA CLUSTERS USING CORRELITHM OBJECTS AND MACHINE LEARNING IN A CORRELITHM OBJECT PROCESSING SYSTEM

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 transform a first data value and a second data value from the set of data value into sub-string correlithm objects. The model training engine is further configured to compute a Hamming distance between the first sub-string correlithm object and the second sub-string correlithm object and to identify a boundary in response to determining that the Hamming distance exceeds a bit difference threshold value. The model training engine is further configured to determine a number of identified boundaries, to determine a number of clusters based on the number of identified boundaries, and to train the machine learning model to associate the determined number of clusters with the feature vector.