Systems and methods to improve data clustering using a meta-clustering model

1. A system for clustering data, comprising:

one or more memory units storing instructions; and

one or more processors configured to execute the instructions to perform operations comprising:

receiving data from a database;

generating, using a plurality of embedding network layers, preliminary data clusters based on the received data;

generating, using a meta-clustering model, final clustered data based on the preliminary data clusters; and

storing the final clustered data.

2. The system of claim 1 , the operations further comprising:

generating an updated embedding network by training an embedding network layer of the embedding network layers; and

generating, using the updated embedding-network, updated clustered data based on the received data.

3. The system of claim 1 , the operations further comprising:

determining, using the meta-clustering model, a number of clusters based on the received data.

4. The system of claim 3 , the operations further comprising:

transmitting clustered data samples to a client device configured to generate a user interface that receives tags from a user, the clustered data samples being based on the clustered data; and

receiving, from the client device, tags associated with the clustered data samples; and wherein determining the number of clusters comprises determining the number of clusters based on the tags.

5. The system of claim 1 , the operations further comprising:

sending, to the client device for display on the user interface, at least one of:

the final clustered data;

a visual representation of the final clustered data;

the number of clusters; or

the meta-clustering model.

6. The system of claim 1 , the operations further comprising:

generating clustered data samples based on the preliminary data clusters; and

generating tags associated with the clustered data samples using the meta-clustering model,

wherein determining the number of clusters is based on the tags.

7. The system of claim 1 , wherein generating preliminary data clusters comprises:

receiving training data;

generating one of the embedding network layers;

training the generated embedding network layer to classify training data; and

training the generated embedding network layer to cluster training data.

8. The system of claim 1 , wherein generating preliminary data clusters comprises repeating generating steps until a performance criterion is satisfied, the generating steps comprising:

adding a trained embedding network layer to the plurality of embedding network layers;

generating clustered data using the added embedding-network layer;

tagging the clustered data; and

determining whether a performance criterion of the embedding networks is satisfied.

9. The system of claim 1 , the operations further comprising:

generating the meta-clustering model;

reducing a dimensionality of the clustered data;

generating, using the meta-clustering model, encoded data based on the reduced-dimensionality clustered data;

generating a data map based on the encoded data; and

training the meta-clustering model to determine a number of clusters based on the data map and a performance criterion.

10. The system of claim 8 , wherein generating, using the meta-clustering model, final clustered data is further based on the number of clusters.

11. The system of claim 8 , wherein training the meta-clustering model comprises iteratively repeating training steps until the performance criterion is satisfied, the training steps comprising:

determining, using the meta-clustering model, a number of clusters based on the data map;

generating an updated embedding network based on the number of clusters;

generating updated clustered-data using the updated embedding network;

updating the meta-clustering model;

reducing a dimensionality of the updated clustered data;

generating, using the updated meta-clustering model, updated encoded data based on the reduced-dimensionality updated clustered data;

generating an updated data map based on the updated encoded data; and

determining whether the performance criterion is satisfied based on the updated data map.

12. The system of claim 1 , wherein the data comprises image data and the embedding network layer comprises a convolutional neural network.

13. The system of claim 1 , wherein the data comprises text data and the embedding network layers comprise a language representation model.

14. The system of claim 1 , wherein the embedding network layers comprise at least one of a Bidirectional Encoder Representations from Transformers (BERT) model or an Embeddings from Language Models (ELMo) representation model.

15. The system of claim 1 , wherein the meta-clustering model comprises a deep learning model.

16. The system of claim 3 , wherein the number of clusters is larger than a maximum count of clusters in the preliminary data clusters.

17. The system of claim 1 , wherein generating preliminary data clusters comprises performing a binary classification.

18. The system of claim 1 , wherein generating preliminary data clusters comprises performing one-hot encoding.

19. A method for clustering data, comprising:

receiving data from a database;

generating, using a plurality of embedding network layers, preliminary data clusters based on the received data;

generating, using a meta-clustering model, final clustered data based on the preliminary data clusters; and

storing the final clustered data.

20. A system for clustering data comprising:

one or more memory units storing instructions; and

one or more processors configured to execute the instructions to perform operations comprising:

generating preliminary data clusters using a plurality of embedding network layers by repeating steps until a performance criterion is satisfied, the steps comprising:

adding a trained embedding network layer;

generating clustered data using the trained embedding network layer;

tagging the clustered data; and

determining whether a performance criterion of the embedding network layers is satisfied;

reducing a dimensionality of the clustered data;

generating, using a meta-clustering model, encoded data based on the reduced-dimensionality clustered data;

generating a data map based on the encoded data; and

generating, using the meta-clustering model, final clustered data based on the data map.