Automatic Clustering Labeling via Frequency and Coverage Metrics
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current machine learning clustering methods lack the ability to provide descriptive labels for grouped data, leading to errors, wasted time, and inaccuracies, particularly in applications like fraud detection where misidentification of trends can result in significant costs.
Innovation Solution
A system is introduced that automatically labels clusters using frequency counts, ratios, and coverage computations, leveraging feature-based dictionaries to determine relevant features and provide comprehensive labels for clusters generated by unsupervised machine learning methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning clustering methods are used to group data, then data classification is achieved, but the clusters lack descriptive labels leading to errors and inaccuracies
Solution Approach 1:
The patent introduces an intermediary labeling system that bridges the gap between clustering algorithms and interpretable results. The system uses frequency dictionaries and coverage computations as mediators to generate descriptive labels for clusters, transforming raw clustered data into labeled, interpretable groups without altering the original clustering structure.
Solution Approach 2:
The patent performs preliminary labeling actions by computing frequency dictionaries and coverage metrics before final cluster interpretation. This preliminary computation of feature frequencies and coverage values prepares the data structure in advance, enabling accurate label generation that preserves classification precision while adding descriptive information.
2Measurement precision
If manual labeling of clusters is performed to improve accuracy, then label precision is enhanced, but time consumption and operational complexity increase
Solution Approach 1:
The patent implements self-service labeling where the system automatically generates cluster labels using frequency dictionaries and coverage computations without requiring manual human intervention. The algorithm serves itself by computing labels from the clustered data structure, maintaining high accuracy while eliminating time-consuming manual labeling processes.
Solution Approach 2:
The patent replaces the mechanical manual labeling process with an automated computational system. Instead of human operators manually examining and labeling clusters, the system uses frequency analysis and coverage computations to automatically generate accurate labels, substituting mechanical human labor with automated information processing.
3Measurement precision
If comprehensive feature analysis is performed to improve label quality, then labeling precision is enhanced, but computational complexity and processing time increase
Solution Approach 1:
The patent extracts only the most relevant features for labeling by computing frequency dictionaries that identify significant features and their coverage. Instead of analyzing all possible features, the system extracts and processes only those features that meet frequency and coverage thresholds, reducing computational complexity while maintaining label quality.
Solution Approach 2:
The patent applies local quality by computing frequency and coverage metrics specifically for features relevant to each cluster rather than uniformly analyzing all features across all data. This localized approach focuses computational resources on the most informative features for each cluster, improving label quality without proportionally increasing overall complexity.
Data Source
AI summary
Aspects of the present disclosure involve systems, methods, devices, and the like for auto-labeling clusters generated by machine learning models. In one embodiment, a system is introduced that can perform a series of operations for determining comprehensive labels for clusters output from machine learning methods used to classify data sets. The auto-labeling system may include generating labels determined using a computation of a frequency count, ratio, and coverage. These computations may use feature-based dictionaries which aid in the determination, storage, and analysis of the relevant features useful in labeling the clusters.