Multi-stage clustering for compliance data labeling
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Solution Overview
Problem
Evaluating large data sets for compliance with changing regulations is resource-intensive and prone to human error, as existing supervised classification algorithms require significant manual labeling and are not well-suited for varying data characteristics.
Innovation Solution
Multi-stage clustering techniques that iteratively group similar data elements into tightly knit clusters, using unsupervised learning models and similarity scoring to reduce manual effort and resource consumption, allowing for prioritization and auto-labeling of data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If supervised classification algorithms are used for compliance evaluation, then classification accuracy can be achieved, but manual labeling effort and resource consumption increase significantly
Solution Approach 1:
The patent applies preliminary action by performing unsupervised clustering before supervised classification. The system pre-processes data by automatically grouping similar items into clusters using unsupervised learning models, which prepares the data structure in advance for more efficient supervised classification. This preliminary clustering reduces the manual labeling burden by organizing data into coherent groups that can be evaluated more systematically.
Solution Approach 2:
The patent introduces an intermediary mechanism by using unsupervised clustering as a bridge between raw data and supervised classification. The clustering process acts as an intermediate step that organizes data into clusters, which then serve as the input for supervised classification. This intermediary structure reduces the direct burden of manual labeling while maintaining classification accuracy.
2Ease of operation
If traditional clustering methods are used, then data grouping can be achieved, but computational resources and time consumption increase for large data sets
Solution Approach 1:
The patent applies segmentation by dividing the large data set into multiple smaller clusters through iterative unsupervised learning. Instead of processing all data at once, the system segments the data into manageable groups in multiple stages, where each stage processes a portion of the data and produces intermediate clusters. This segmentation reduces computational resource consumption by breaking down the complex task of clustering large data sets into smaller, more efficient sub-tasks.
Solution Approach 2:
The patent implements dynamics by using iterative clustering where the clustering process is performed in multiple stages with progressively refined groupings. The system dynamically adjusts cluster formations across iterations, starting with broader groupings and progressively creating more granular clusters. This dynamic approach optimizes computational efficiency by adapting the clustering granularity to the specific characteristics of the data at each stage.
3Measurement precision
If manual compliance review is performed, then detailed assessment can be conducted, but human error and inconsistency increase
Solution Approach 1:
The patent applies feedback by implementing an iterative clustering process where the results of each clustering stage are evaluated and used to inform subsequent clustering operations. The system incorporates feedback loops that assess cluster quality and adjust clustering parameters accordingly. This feedback mechanism reduces human error and inconsistency by systematically evaluating and refining cluster formations based on measured performance criteria rather than subjective human judgment.
Data Source
AI summary
Systems and techniques are generally described for multi-stage text-based clustering. In various examples, a first data set comprising a first set of elements may be received. Each element of the first set of elements may include text data. A clustering algorithm may be used to determine a first set of clusters of the first set of elements based at least in part on similarities in the text data. A second set of clusters, for each cluster of the first set of clusters, may be determined using the clustering algorithm. A number of resources for labeling the first set of elements may be determined based at least in part on the first set of clusters. The number of resources may be assigned to label clusters of the second set of clusters based at least in part on a number of elements in each cluster of the second set of clusters.