APPARATUS AND METHODS FOR INCREASING PROXIMITY OF A SUBJECT PROCESS TO AN OUTLIER CLUSTER

1 . An apparatus for determining an instruction set, the apparatus comprising:

a processor;

a memory connected to the processor, the memory containing instructions configuring the processor to:

receive, from a user device, a plurality of datasets, wherein each dataset of the plurality of datasets describes a plurality of actions performed by an entity and a sub-entity;

generate, for each dataset of the plurality of datasets, a plurality of outlier clusters, wherein a first outlier cluster pertains to the entity and a second outlier cluster pertains to the sub-entity;

generate a harmonized outlier cluster as a function of the plurality of outlier clusters;

classify the harmonized outlier cluster to at least a label of label data using a classifier, wherein classifying the harmonized outlier cluster comprises:

training the classifier using training data, wherein a training data classifier is configured to classify elements of the training data to a plurality of sub-entities; and

classifying the harmonized outlier cluster to the at least a label using the trained classifier; and

generate an interface data structure, wherein the interface data structure configures a remote display device to display the harmonized outlier cluster and the at least a label.

2 . The apparatus of claim 1 , wherein the plurality of actions comprises a mindset of the entity.

3 . The apparatus of claim 1 , wherein classifying the harmonized outlier cluster to the at least a label further comprises classifying the harmonized outlier cluster to the at least a label of the label data based at least in part on a threshold value.

4 . The apparatus of claim 1 , wherein generating the interface data structure comprises:

retrieving user attribute data; and

displaying a representation of a first label and a second label of the at least a label in a grid based on the user attribute data.

5 . The apparatus of claim 4 , wherein generating the interface data structure comprises generating the interface data structure based on the user attribute data, wherein generating the interface data structure further comprises:

determining a vector from the representation of the first label to the second label; and

configuring the remote display device to display the vector.

6 . The apparatus of claim 1 , wherein the memory contains instructions further configuring the processor to:

receive, from the user device, a subject process describing a current state of the sub-entity;

identify, for the second outlier cluster of each dataset, a process modification model, wherein the process modification model describes a set of actions to be performed to increase a proximity of the subject process to the second outlier cluster; and

display the subject process using the interface data structure.

7 . The apparatus of claim 6 , wherein the interface data structure further configures the remote display device to:

display an input field;

receive a user-input datum from the input field, wherein the user-input datum describes data for updating the subject process; and

display an instruction set determined using the process modification model, wherein displaying the instruction set comprises displaying an updated subject process based on the user-input datum.

8 . The apparatus of claim 1 , wherein generating the harmonized outlier cluster further comprises measuring how well each data element of each dataset of the plurality of datasets fits into an assigned cluster of the harmonized outlier cluster, wherein measuring further comprises:

aggregating data comprised in each dataset of the plurality of datasets;

identifying data within the aggregated data based on similarity;

matching at least a portion of the plurality of actions to a threshold value;

assigning a quality score for the identified data based on assessing whether the identified data exceeds the threshold value; and

generating the harmonized outlier cluster as a function of the quality score.

9 . The apparatus of claim 1 , wherein training the classifier using the training data comprises iteratively training the classifier as a function of previous iterations.

10 . The apparatus of claim 1 , wherein generating the harmonized outlier cluster further comprises:

determining at least an activity pattern as a function of the plurality of datasets; and

generating the harmonized outlier cluster as a function of the at least an activity pattern.

11 . A method for determining an instruction set, the method comprising:

receiving, using a processor and from a user device, a plurality of datasets, wherein each dataset of the plurality of datasets describes a plurality of actions performed by an entity and a sub-entity;

generating, using the processor and for each dataset of the plurality of datasets, a plurality of outlier clusters, wherein a first outlier cluster pertains to the entity and a second outlier cluster pertains to the sub-entity;

generating, using the processor, a harmonized outlier cluster as a function of the plurality of outlier clusters;

classifying, using the processor, the harmonized outlier cluster to at least a label of label data using a classifier, wherein classifying the harmonized outlier cluster comprises:

training the classifier using training data, wherein a training data classifier is configured to classify elements of the training data to a plurality of sub-entities; and

classifying the harmonized outlier cluster to the at least a label using the trained classifier; and

generating, using the processor, an interface data structure, wherein the interface data structure configures a remote display device to display the harmonized outlier cluster and the at least a label.

12 . The method of claim 11 , wherein the plurality of actions comprises a mindset of the entity.

13 . The method of claim 11 , wherein classifying the harmonized outlier cluster to the at least a label further comprises classifying the harmonized outlier cluster to the at least a label of the label data based at least in part on a threshold value.

14 . The method of claim 11 , wherein generating the interface data structure comprises:

retrieving user attribute data; and

displaying a representation of a first label and a second label of the at least a label in a grid based on the user attribute data.

15 . The method of claim 14 , wherein generating the interface data structure comprises generating the interface data structure based on the user attribute data, wherein generating the interface data structure further comprises:

determining a vector from the representation of the first label to the second label; and

configuring the remote display device to display the vector.

16 . The method of claim 11 , further comprising:

receiving, using the processor and from the user device, a subject process describing a current state of the sub-entity associated with the user device;

identifying, using the processor and for the second outlier cluster of each dataset, a process modification model, wherein the process modification model describes a set of actions to be performed to increase a proximity of the subject process to the second outlier cluster; and

displaying, using the processor, the subject process using the interface data structure.

17 . The method of claim 16 , wherein the interface data structure further configures the remote display device to:

display an input field;

receive a user-input datum from the input field, wherein the user-input datum describes data for updating the subject process; and

display an instruction set determined using the process modification model, wherein displaying the instruction set comprises displaying an updated subject process based on the user-input datum.

18 . The method of claim 11 , wherein generating the harmonized outlier cluster further comprises measuring how well each data element of each dataset of the plurality of datasets fits into an assigned cluster of the harmonized outlier cluster, wherein measuring further comprises:

aggregating data comprised in each dataset of the plurality of datasets;

identifying data within aggregated data based on similarity and matching at least a portion of the plurality of actions;

assigning a quality score for the identified data based on assessing whether the identified data exceeds a threshold value; and

generating the outlier cluster as a function of the quality score.

19 . The method of claim 11 , wherein training the classifier using the training data comprises iteratively training the classifier as a function of previous iterations.

20 . The method of claim 11 , wherein generating the harmonized outlier cluster further comprises:

determining at least an activity pattern as a function of the plurality of datasets; and

generating the harmonized outlier cluster as a function of the at least an activity pattern.