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

Organization Name

Inventor(s)

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.

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

Contents

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

Organization Name

Inventor(s)