The goal of this research was to determine if the results of a simple comparison algorithm (SCA) could be improved by adding a hyperspace analogue to language model of memory (HAL) layer to form NCA. The HAL layer provides contextual data that otherwise would be unavailable for consideration. It was found that NCA did improve the results when compared to SCA alone. However, NCA added complexity problems that limit its practicality. The complexity of this algorithm is On3 where n is equal to the number of unique symbols in the data. While there is a relativity reasonable soft upper bound for the number of unique symbols used in a language, the complexity still limits the uses of the NCA combined algorithm. The conclusion from this research is that NCA can improve results. This research also suggested that the quality of results might increase as more data is processed by NCA.