View source for 18155529. SYSTEMS AND METHODS TO IMPROVE DATA CLUSTERING USING A META-CLUSTERING MODEL simplified abstract (Capital One Services, LLC)

=SYSTEMS AND METHODS TO IMPROVE DATA CLUSTERING USING A META-CLUSTERING MODEL=


==Organization Name==

[[:Category:Capital One Services, LLC|Capital One Services, LLC]]


[[Category:Capital One Services, LLC]]

==Inventor(s)==

[[:Category:Austin Walters of Savoy IL (US)|Austin Walters of Savoy IL (US)]][[Category:Austin Walters of Savoy IL (US)]]

[[:Category:Jeremy Goodsitt of Champaign IL (US)|Jeremy Goodsitt of Champaign IL (US)]][[Category:Jeremy Goodsitt of Champaign IL (US)]]

[[:Category:Anh Truong of Champaign IL (US)|Anh Truong of Champaign IL (US)]][[Category:Anh Truong of Champaign IL (US)]]

[[:Category:Reza Farivar of Champaign IL (US)|Reza Farivar of Champaign IL (US)]][[Category:Reza Farivar of Champaign IL (US)]]

==SYSTEMS AND METHODS TO IMPROVE DATA CLUSTERING USING A META-CLUSTERING MODEL - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18155529 titled 'SYSTEMS AND METHODS TO IMPROVE DATA CLUSTERING USING A META-CLUSTERING MODEL

==Simplified Explanation==

The patent application describes systems and methods for clustering data using embedding network layers and a meta-clustering model. Here is a simplified explanation of the abstract:

* The system includes memory units and processors to perform operations on data received from a client device.
* Preliminary clustered data is generated using embedding network layers, which analyze the received data.
* A data map is created based on the preliminary clustered data using a meta-clustering model.
* The meta-clustering model determines the number of clusters in the data map.
* Final clustered data is generated based on the determined number of clusters using the meta-clustering model.
* The final clustered data is then transmitted back to the client device.

== Potential Applications ==
This technology can have various applications, including:

* Data analysis: The clustering of data can help in identifying patterns and relationships within large datasets, enabling better analysis and decision-making.
* Recommendation systems: By clustering user preferences and behavior, this technology can enhance recommendation systems by providing more accurate and personalized recommendations.
* Image and video processing: Clustering can be used to group similar images or videos together, aiding in tasks such as image recognition, video summarization, and content organization.

== Problems Solved ==
The technology addresses the following problems:

* Data clustering complexity: Traditional clustering algorithms may struggle with large and complex datasets. This technology offers a more efficient and scalable approach to clustering data.
* Determining the number of clusters: The meta-clustering model helps in automatically determining the optimal number of clusters, removing the need for manual intervention or trial-and-error.
* Accuracy and reliability: By utilizing embedding network layers and a meta-clustering model, the system aims to provide more accurate and reliable clustering results compared to traditional methods.

== Benefits ==
The technology offers several benefits, including:

* Improved efficiency: The use of embedding network layers and a meta-clustering model allows for faster and more efficient clustering of data.
* Automation: The system automates the process of determining the number of clusters, reducing the need for manual intervention and saving time.
* Enhanced accuracy: By leveraging advanced techniques, the technology aims to provide more accurate and reliable clustering results, leading to better insights and decision-making.


==Original Abstract Submitted==

Systems and methods for clustering data are disclosed. For example, a system may include one or more memory units storing instructions and one or more processors configured to execute the instructions to perform operations. The operations may include receiving data from a client device and generating preliminary clustered data based on the received data, using a plurality of embedding network layers. The operations may include generating a data map based on the preliminary clustered data using a meta-clustering model. The operations may include determining a number of clusters based on the data map using the meta-clustering model and generating final clustered data based on the number of clusters using the meta-clustering model. The operations may include and transmitting the final clustered data to the client device.



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