Decision making using integrated machine learning models and knowledge graphs

A method comprising:

• at a computing platform comprising at least one processor, a communication interface, and memory:
• training, using historical event processing data, a machine learning model, wherein training the machine learning model comprises clustering the historical event processing data based on common characteristics, and wherein training the machine learning model configures the machine learning model to cluster input data and label the input data based on the corresponding cluster;
• generating, using the historical event processing data, a knowledge graph, wherein generating the knowledge graph comprises using a depth first search method to create graph clusters configured to generate correlations between information stored in the machine learning model and additional information stored in the knowledge graph;
• generating a correlation matrix, wherein the correlation matrix includes correlation values between Euclidian distances between data points of the machine learning model and hop distances between nodes of the knowledge graph;
• receiving, by the at least one processor, new event processing data;
• identifying, by the at least one processor and using the machine learning model, k nearest data points corresponding to the new event processing data;
• identifying, by the at least one processor and using the knowledge graph, k nearest data nodes corresponding to the new event processing data;
• generating, by the at least one processor and using the correlation values, first weighted relative distances between the new event processing data and the k nearest data points;
• generating, by the at least one processor and using the correlation values, second weighted relative distances between the new event processing data and the k nearest data nodes;
• identifying, by the at least one processor and based on the first weighted relative distances and the second weighted relative distances, a data cluster for the new event processing data;
• sending, by the at least one processor and based on the identified data cluster, event processing information and one or more commands directing an enterprise computing device to display the event processing information, wherein sending the one or more commands directing the enterprise computing device to display the event processing information causes the enterprise computing device to display the event processing information;
• and
• update, based on the identified data cluster and the new event processing data, the machine learning model and the knowledge graph.

One or more non-transitory computer-readable media storing instructions that, when executed by a computing platform comprising at least one processor, a communication interface, and memory, cause the computing platform to:

• train, using historical event processing data, a machine learning model, wherein training the machine learning model comprises clustering the historical event processing data based on common characteristics, and wherein training the machine learning model configures the machine learning model to cluster input data and label the input data based on the corresponding cluster;
• generate, using the historical event processing data, a knowledge graph, wherein generating the knowledge graph comprises using a depth first search method to create graph clusters configured to generate correlations between information stored in the machine learning model and additional information stored in the knowledge graph;
• generate a correlation matrix, wherein the correlation matrix includes correlation values between Euclidian distances between data points of the machine learning model and hop distances between nodes of the knowledge graph;
• receive new event processing data;
• identify, using the machine learning model, k nearest data points corresponding to the new event processing data;
• identify, using the knowledge graph, k nearest data nodes corresponding to the new event processing data;
• generate, using the correlation values, first weighted relative distances between the new event processing data and the k nearest data points;
• generate, using the correlation values, second weighted relative distances between the new event processing data and the k nearest data nodes;
• identify, based on the first weighted relative distances and the second weighted relative distances, a data cluster for the new event processing data;
• send, based on the identified data cluster, event processing information and one or more commands directing an enterprise computing device to display the event processing information, wherein sending the one or more commands directing the enterprise computing device to display the event processing information causes the enterprise computing device to display the event processing information;
• and
• update, based on the identified data cluster and the new event processing data, the machine learning model and the knowledge graph.