Parameter set determination for clustering of datasets

A method for hyper-optimizing a clustering engine, comprising:

• generating a plurality of cluster solutions corresponding to a parameter set of a dataset;
• obtaining a Fitness score for the parameter set;
• storing the parameter set in a temporary storage;
• changing at least one parameter value of the parameter set;
• and
• iterating through the generating, obtaining, storing and changing steps until an average fitness of a current generation of the parameter set has stabilized to within a given percentage of a previous generation's average fitness;
• selecting a cluster solution having a maximum total score from a plurality of cluster solutions as a hyper-optimized cluster solution, the plurality of cluster solutions generated using the current generation of the parameter set;
• and
• generating an actionable item based on the hyper-optimized cluster solution.

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

• generate a plurality of clusters corresponding to a parameter set of a dataset;
• obtain a Fitness score for the parameter set;
• store the parameter set in a temporary storage;
• change at least one parameter value of the parameter set;
• iterate through the generating, obtaining, storing and changing when a parameter set is generated through the change that has a higher Fitness score until a predetermined number of iterations are performed;
• select a cluster having a maximum total score from a plurality of clusters as a hyper-optimized cluster, the plurality of clusters are associated with one parameter set of the predetermined number of parameter sets, the associated parameter set having a high Fitness score;
• and
• perform an actionable item based on the hyper-optimized cluster.