Automated Data Segmentation via Linear Regression
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current data segmentation processes are manual, slow, inefficient, and dependent on user judgment, lacking automation and predictive capabilities.
Innovation Solution
A data segmentation system using machine learning that employs linear regression to analyze parameter values, ranks parameters by signal strength, and assigns weights to automatically segment data into categories, enabling predictive and automated data segmentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual data segmentation is performed by users, then flexibility in selecting parameters is maintained, but the process becomes slow and inefficient
Solution Approach 1:
The system performs self-service by automatically selecting segmentation parameters and generating segmentations without requiring manual user input. The machine learning model independently analyzes the data and determines optimal segmentation parameters, eliminating the slow manual process while maintaining or improving flexibility through algorithmic adaptability.
Solution Approach 2:
The manual mechanical process of user-based parameter selection is replaced with an automated machine learning system. The ML model substitutes human judgment with computational analysis, dramatically increasing segmentation speed while maintaining flexibility through the model's ability to adapt to different data characteristics.
2Reliability
If manual data segmentation is performed, then user judgment can be applied, but the process is dependent on individual user expertise and consistency
Solution Approach 1:
The inconsistent human judgment process is replaced with a standardized machine learning system. The ML model applies consistent algorithms and criteria across all segmentation tasks, eliminating variability introduced by different users while maintaining high reliability through proven machine learning techniques.
Solution Approach 2:
The system changes from fixed manual parameters to dynamic parameters determined by the ML model. The model can adapt parameters based on data characteristics, providing both consistency through standardized processing and flexibility through algorithmic adaptation to different data types and patterns.
3Productivity
If automated machine learning segmentation is implemented, then productivity and consistency are improved, but the system complexity increases
Solution Approach 1:
Complex manual processes are replaced with automated machine learning systems that, while technically complex, provide significant productivity gains. The ML system handles parameter selection, segmentation generation, and optimization automatically, reducing manual effort despite the computational complexity involved.
Solution Approach 2:
The machine learning model acts as an intermediary between raw data and segmented results. It manages the complexity of automated segmentation by providing a standardized interface that handles parameter selection and segmentation generation, making the complex process transparent and manageable for users.
Data Source
AI summary
Disclosed are systems, methods, and non-transitory computer-readable media for data segmentation using machine learning. A data segmentation system prioritizes parameters used for segmenting data into predetermined categories. For example, the data segmentation system uses linear regression to determine signal strength values for the individual parameters. The signal strength values can be used to automatically select a set of parameters for segmenting data, determine weights for the parameters and/or determine threshold segmentation values.