Data Clustering via Human Supervision and Disentanglement
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Solution Overview
Problem
Conventional data clustering techniques are resource-intensive and often produce inaccurate results due to the number of analysis iterations required and unnecessary use of power, communication, processing, and storage resources, failing to optimally disentangle and classify unstructured heterogeneous data effectively.
Innovation Solution
A computer-implemented method that processes heterogeneous data using a data clustering algorithm to obtain an initial data cluster, then refines it through human supervision by comparing machine-obtained and human-obtained results, modifying the algorithm to improve accuracy and efficiency, and applying disentanglement matrices to subsequent data corpora for improved clustering and learning speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional data clustering techniques are used to process heterogeneous data, then data can be organized into clusters, but the process requires excessive computational resources and produces inaccurate results due to multiple analysis iterations
Solution Approach 1:
The patent applies preliminary action by performing human supervision and validation on a subset of data before full-scale clustering. Human analysts pre-validate clustering results on sample data to establish accurate ground truth, which then guides and improves automated clustering algorithms on the complete dataset, reducing the need for multiple resource-intensive iterations.
Solution Approach 2:
The patent implements feedback mechanisms where human supervision results are fed back into the clustering algorithm to continuously improve its performance. The system compares automated clustering results with human-validated results, uses the discrepancies to refine algorithm parameters, and iterates until convergence, thereby improving accuracy while reducing overall computational resource consumption.
2Measurement precision
If multiple analysis iterations are performed to improve clustering accuracy, then more accurate clusters are obtained, but resource consumption including power, communication, processing, and storage increases unnecessarily
Solution Approach 1:
Human supervision is performed preliminarily on a representative subset of data to establish accurate clustering ground truth before processing the complete dataset. This preliminary human validation creates a reference framework that guides subsequent automated processing, eliminating the need for multiple full-scale iterations and thereby improving processing efficiency while maintaining high accuracy.
Solution Approach 2:
The patent applies partial action by performing human supervision only on a subset of data rather than the entire dataset. This selective approach provides sufficient guidance for accurate clustering without the excessive resource consumption that would result from applying the same level of analysis to all data points, thus optimizing the balance between accuracy and productivity.
3Productivity
If conventional clustering algorithms process heterogeneous data without human supervision, then processing speed is maintained, but the ability to disentangle and classify unstructured data effectively is insufficient
Solution Approach 1:
The patent introduces human analysts as an intermediary between raw heterogeneous data and automated clustering algorithms. These human intermediaries validate and annotate sample data, creating a bridge that translates unstructured heterogeneous data into structured training examples that guide the automated algorithms, thereby improving classification accuracy without significantly compromising processing speed.
Solution Approach 2:
The system implements self-service by using human-validated sample data to automatically train and refine clustering algorithms that then process the complete dataset independently. Once the algorithms are trained on human-validated samples, they autonomously perform high-speed clustering on the remaining data without requiring continuous human supervision, thus maintaining processing speed while improving accuracy.
Data Source
AI summary
A set of data comprising heterogeneous data is processed in accordance with a data clustering algorithm so as to obtain an initial data cluster comprising homogeneous data. A supervised data cluster generated based on a human analysis of the set of data is obtained and compared with the initial data cluster to obtain a comparison result. The data clustering algorithm is modified based on the comparison result.