APPARATUS AND METHOD FOR LOCAL OPTIMIZATION USING UNSUPERVISED LEARNING

1 . An apparatus for local optimization using unsupervised learning, the apparatus comprises:

at least a processor; and

a memory communicatively connected to the at least a processor, the memory containing instruction configuring the at least processor to:

identify a first stage of a process, wherein the first stage includes:

a plurality of candidate subsequent stages; and

a plurality of potential resources, each resource of the plurality of potential resources having a plurality of attributes;

select an optimal resource of the plurality of potential resources, wherein selecting further comprises:

receiving optimal resource training data, wherein the optimal resource training data correlates a plurality of attributes to at least an attribute cluster;

generating, for each resource of the plurality of potential resources, using a clustering algorithm, the optimal resource training data, and the plurality of attributes, at least an attribute cluster;

identifying, for a resource of the plurality of potential resources, an outlier cluster of the at least an attribute cluster; and

selecting the optimal resource based on the outlier cluster and wherein the optimal resource meets the specific needs of a given process selected from a range of options;

apply a local optimization constraint to a local optimization process, wherein the local optimization constraint comprises the optimal resource; and

identify a subsequent stage of the plurality of stages using the local optimization process.

2 . The apparatus of claim 1 , wherein selecting the optimal resource further comprises using an unsupervised learning algorithm to analyze the plurality of attributes.

3 . The apparatus of claim 1 , wherein the memory contains instructions further configuring the at least a processor to select at least a relevant attribute from the plurality of attributes using a selection mechanism.

4 . The apparatus of claim 1 , wherein identifying the subsequent stage further comprises utilizing an adjustment algorithm to process real-time data as a function of real-time data.

5 . The apparatus of claim 1 , wherein the apparatus is further configured to automatically update the optimal resource as a function of a change in process.

6 . The apparatus of claim 1 , wherein the memory contains instructions configuring the at least a processor to generate a display data structure, wherein the display data structure comprises the subsequent stage.

7 . The apparatus of claim 1 , wherein the memory contains instructions further configuring the at least a processor to:

generate the optimal resource using an optimal resource machine-learning model, wherein the optimal resource machine-learning model is trained using optimal resource training data;

receive user feedback; and

adjust the optimal resource training data as a function of the user feedback.

8 . The apparatus of claim 1 , wherein the apparatus is further configured to update the optimal resource and local optimization constraints as a function of real-time data.

9 . The apparatus of claim 1 , wherein the apparatus is configured to generate a comparison report between the potential resources and the optimal resources.

10 . The apparatus of claim 1 , wherein the local optimization process comprises:

receiving local optimization training data, wherein the local optimization training data comprises associated preceding stages and local optimization constraints correlated to subsequent stages;

training a local optimization machine-learning model using the local optimization training data; and

determining, using the local optimization machine-learning model, the subsequent stage of the plurality of stages.

11 . A method for local optimization using unsupervised learning, the method comprising:

identifying, using at least a processor, a first stage of process, wherein the first stage includes:

a plurality of candidate subsequent stages; and

a plurality of potential resources, each resource of the plurality of resources having a plurality of attributes;

selecting, using the at least a processor, an optimal resource of the plurality of potential resources, wherein selecting further comprises:

receiving optimal resource training data, wherein the optimal resource training data correlates a plurality of attributes to at least an attribute cluster;

generating, for each resource of the plurality of potential resources, using a clustering algorithm, the optimal resource training data, and the plurality of attributes, at least an attribute cluster;

identifying, for a resource of the plurality of potential resources, an outlier cluster of the at least an attribute cluster; and

selecting the optimal resource based on the outlier cluster and wherein the optimal resource meets the specific needs of a given process selected from a range of options;

applying, using the at least a processor, a local optimization constraint to a local optimization process, wherein the location optimization constraint comprises the selected optimal resources; and

identifying, using the at least a processor, a subsequent stage of the plurality of stages using a local optimization process having the local optimization constraint.

12 . The method of claim 11 , wherein selecting the optimal resource further comprises using an unsupervised learning algorithm to analyze the plurality of attributes.

13 . The method of claim 11 , further comprising selecting, using the at least a processor, at least a relevant attribute from the plurality of attributes using a selection mechanism.

14 . The method of claim 11 , wherein identifying the subsequent stage further comprises an adjustment algorithm as a function of real-time data.

15 . The method of claim 11 , further comprising updating, using the at least a processor, the optimal resource and in response to change in process.

16 . The method of claim 11 , further comprising generating, using the at least a processor, a display data structure, wherein the display data structure comprises the subsequent stage.

17 . The method of claim 11 , further comprising:

generating, using the at least a processor, the optimal resource using an optimal resource machine-learning model, wherein the optimal resource machine-learning model is trained using optimal resource training data;

receiving, using the at least a processor, user feedback; and

adjusting, using the at least a processor, the optimal resource training data as a function of the user feedback.

18 . The method of claim 11 , further comprising updating, using the at least a processor, the optimal resource and local optimization constraints as a function of real-time data.

19 . The method of claim 11 , further comprising generating, using the at least a processor, a comparison report between the potential resources and the optimal resources.

20 . The method of claim 11 , wherein the local optimization process comprises:

receiving local optimization training data, wherein the local optimization training data comprises associated preceding stages and local optimization constraints correlated to subsequent stages;

training a local optimization machine-learning model using the local optimization training data; and

determining, using the local optimization machine-learning model, the subsequent stage of the plurality of stages.