SYSTEMS AND METHODS FOR DATA STRUCTURE GENERATION BASED ON OUTLIER CLUSTERING
Disclosed herein are systems and methods for determining data structures. In some embodiments, a classifier may be used to determine one or more attributes of an entity. In some embodiments, a clustering algorithm may be used to determine an attribute cluster. In some embodiments, an impact metric machine learning model may be used to determine an outlier cluster. In some embodiments, an outlier process may be determined as a function of the outlier cluster. In some embodiments, a visual element may be determined as a function of an outlier process and may be displayed to a user.
1 . An apparatus for data structure generation, the apparatus comprising:
at least a processor; and
a memory communicatively connected to the at least processor, the memory containing instructions configuring the at least processor to:
identify a plurality of attribute clusters;
locate in the plurality of attribute clusters an outlier cluster, wherein the outlier cluster is an attribute cluster of the plurality of attribute clusters;
determine an outlier process as a function of an outlier cluster;
determine a visual element data structure as a function of the outlier process, wherein the visual element data structure is configured to generate a visual element as a function of at least one of the outlier cluster and the outlier process; and
configure a user device to display the visual element to a user.
2 . The apparatus of claim 1 , wherein locating in the plurality of attribute clusters an outlier cluster comprises:
generating a first impact metric associated with a first attribute cluster of the plurality of attribute clusters and a second impact metric associated with a second attribute cluster of the plurality of attribute clusters; and
determining an outlier cluster as a function of the first impact metric and the second impact metric.
3 . The apparatus of claim 2 , wherein generating the first impact metric further comprises:
inputting the first attribute cluster into an impact metric machine learning model; and
receiving a first impact metric from the impact metric machine learning model.
4 . The apparatus of claim 3 wherein generating the first impact metric further comprises identifying a target process; and
imputing the target process into the impact machine-learning model.
5 . The apparatus of claim 2 , wherein the impact metric indicates higher aptitude in the attribute cluster than the population average.
6 . The apparatus of claim 2 , wherein locating in the plurality of attribute clusters an outlier cluster further comprises:
identifying an external attribute cluster;
inputting the external attribute cluster into the impact metric machine learning model;
receiving an external impact metric from the impact metric machine learning model; and
determining an outlier cluster as a function of an impact metric and an external impact metric.
7 . The apparatus of claim 2 , wherein the impact metric indicates higher aptitude in the attribute cluster than the external impact metric.
8 . The apparatus of claim 1 , wherein determining an outlier process as a function of an outlier cluster comprises:
inputting an outlier cluster into an outlier process machine learning model; and
receiving an outlier process from the outlier process machine learning model.
9 . The apparatus of claim 7 further comprising:
receiving historical attribute clusters associated with historical processes; and
training the outlier process machine-learning model using the historical attribute clusters associated with historical processes.
10 . The apparatus of claim 1 , wherein the visual element is configured to highlight the outlier process.
11 . A method of data structure generation, the method comprising:
identifying, by at least a processor, a plurality of attribute clusters;
locating, by at least a processor, in the plurality of attribute clusters an outlier cluster, wherein the outlier cluster is an attribute cluster of the plurality of attribute clusters;
determining, by at least a processor, an outlier process as a function of an outlier cluster;
determining, by at least a processor, a visual element data structure as a function of the outlier process, wherein the visual element data structure is configured to generate a visual element as a function of at least one of the outlier cluster and the outlier process; and
configuring, by at least a processor, a user device to display the visual element to a user.
12 . The method of claim 11 , wherein locating in the plurality of attribute clusters an outlier cluster comprises:
generating a first impact metric associated with a first attribute cluster of the plurality of attribute clusters and a second impact metric associated with a second attribute cluster of the plurality of attribute clusters; and
determining an outlier cluster as a function of the first impact metric and the second impact metric.
13 . The method of claim 12 , wherein generating the first impact metric further comprises:
inputting the first attribute cluster into an impact metric machine learning model; and
receiving a first impact metric from the impact metric machine learning model.
14 . The method of claim 13 wherein generating the first impact metric further comprises identifying a target process; and
imputing the target process into the impact machine-learning model.
15 . The method of claim 12 , wherein the impact metric indicates higher aptitude in the attribute cluster than the population average.
16 . The method of claim 12 , wherein locating in the plurality of attribute clusters an outlier cluster further comprises:
identifying an external attribute cluster;
inputting the external attribute cluster into the impact metric machine learning model;
receiving an external impact metric from the impact metric machine learning model; and
determining an outlier cluster as a function of an impact metric and an external impact metric.
17 . The method of claim 12 , wherein the impact metric indicates higher aptitude in the attribute cluster than the external impact metric.
18 . The method of claim 11 , wherein determining an outlier process as a function of an outlier cluster comprises:
inputting an outlier cluster into an outlier process machine learning model; and
receiving an outlier process from the outlier process machine learning model.
19 . The method of claim 17 further comprising:
receiving historical attribute clusters associated with historical processes; and
training the outlier process machine-learning model using the historical attribute clusters associated with historical processes.
20 . The method of claim 11 , wherein the visual element is configured to highlight the outlier process.