Network optimization

A method comprising:

• receiving, by a processor, a query from a user, the query to indicate a cluster prediction requirement;
• determining, by the processor, a first node conglomerate by sorting a first dataset into a first plurality of nodes, each of the first plurality of nodes being associated with the cluster prediction requirement;
• determining, by the processor, a plurality of attributes by sorting a second dataset associated with the cluster prediction requirement, the plurality of attributes to be associated with the cluster prediction requirement;
• determining, by the processor, a second node conglomerate for each of the plurality of attributes, each of the second node conglomerate to include a second plurality of nodes associated with each of the plurality of attributes;
• assigning, by the processor, a node confidence score to each of the second plurality of nodes associated with each of the plurality of attributes;
• determining, by the processor, a node graph based on a comparison between the first node conglomerate and the second node conglomerate for each of the plurality of attributes, the node graph to include the first node conglomerate mapped to the second node conglomerate for each of the plurality of attributes through a plurality of mapped connections;
• determining, by the processor, a map confidence index from the node graph, the map confidence index including a confidence value assigned to each of the plurality of mapped connections based on the node confidence score of the each of the second plurality of nodes associated with a corresponding mapped connection from each of the plurality of mapped connections;
• modifying, by the processor, the node graph iteratively based on a node optimization threshold value to generate a harmonized node graph, the processing of the cluster prediction requirement being based on the harmonized node graph;
• and
• creating, by the processor, a node graph library, by associating the plurality of attributes, the second plurality of nodes associated with each of the plurality of attributes, the harmonized node graph, and the map confidence index with the cluster prediction requirement.

A non-transitory computer readable medium including machine readable instructions that are executable by a processor to:

• receive a query from a user, the query to indicate a cluster prediction requirement;
• determine a first node conglomerate by sorting a first dataset into a first plurality of nodes, each of the first plurality of nodes being associated with the cluster prediction requirement;
• determine a plurality of attributes by sorting a second dataset associated with the cluster prediction requirement, the plurality of attributes to be associated with the cluster prediction requirement;
• determine a second node conglomerate for each of the plurality of attributes, each of the second node conglomerate to include a second plurality of nodes associated with each of the plurality of attributes;
• assign a node confidence score to each of the second plurality of nodes associated with each of the plurality of attributes;
• determine a node graph based on a comparison between the first node conglomerate and the second node conglomerate for each of the plurality of attributes, the node graph to include the first node conglomerate mapped to the second node conglomerate for each of the plurality of attributes through a plurality of mapped connections;
• determine a map confidence index from the node graph, the map confidence index including a confidence value assigned to each of the plurality of mapped connections based on the node confidence score of the each of the second plurality of nodes associated with a corresponding mapped connection from each of the plurality of mapped connections;
• modify the node graph iteratively based on a node optimization threshold value to generate a harmonized node graph, the processing of the cluster prediction requirement being based on the harmonized node graph;
• and
• create a node graph library, by associating the plurality of attributes, the second plurality of nodes associated with each of the plurality of attributes, the harmonized node graph, and the map confidence index with the cluster prediction requirement.