Iterative Sampling Clustering for Linear-Scale Dataset Processing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing dataset clustering methods face challenges with nonlinear computational complexity, leading to unacceptably long times for clustering large datasets, especially in applications requiring real-time virtual assistance and system breach mitigation.
Innovation Solution
An iterative sampling based dataset clustering approach that utilizes a fixed sample size independent of the dataset's size, achieving linear computational complexity, allowing for efficient clustering with minimal iterations and real-time topic identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional clustering methods are used on large datasets, then clustering can be performed, but computational complexity becomes nonlinear and clustering time becomes unacceptably long
Solution Approach 1:
The patent divides the large dataset into multiple smaller subsets and processes them in parallel using multiple processors. Each processor handles a specific subset of data, reducing the computational burden on each individual processor while maintaining overall clustering efficiency. This segmentation approach transforms the nonlinear complexity into manageable linear operations across distributed computing units.
Solution Approach 2:
The patent introduces a distributed computing dimension by assigning different data subsets to different processors. This dimensional shift from sequential processing to parallel processing across multiple computational dimensions enables linear scalability. The system achieves O(n) complexity by distributing work across P processors, where each processor handles approximately n/P items, transforming the computational landscape from nonlinear to linear time complexity.
2Loss of time
If iterative sampling with fixed sample size is used, then computational complexity becomes linear and clustering time is reduced, but the accuracy of topic identification may be affected
Solution Approach 1:
The patent performs preliminary clustering on a fixed-size sample of data to establish initial topics and cluster structures before processing the entire dataset. This preliminary action on a representative sample allows the system to pre-compute topic models and clustering parameters that can be efficiently applied to all data items, reducing overall processing time while maintaining accuracy through the representativeness of the sample.
Solution Approach 2:
The patent employs self-service mechanisms where the clustering algorithm automatically adjusts and refines topic assignments based on feedback from the data distribution. The system performs iterative refinement where unassigned or misassigned items are automatically re-evaluated and reassigned to appropriate topics, enabling the system to maintain high accuracy without requiring manual intervention or processing the entire dataset sequentially.
3Speed
If real-time clustering is required for virtual assistance and security monitoring, then response time must be minimal, but processing large datasets quickly becomes difficult
Solution Approach 1:
The patent segments the data processing task across multiple processors, where each processor independently handles a subset of data items. This segmentation enables parallel processing that significantly reduces response time for large datasets. Each processor can simultaneously process its assigned subset without blocking others, achieving real-time performance for virtual assistance and security monitoring applications.
Solution Approach 2:
The patent transitions from sequential processing to parallel processing by introducing a distributed computational dimension. Multiple processors operate simultaneously on different data subsets, transforming the time complexity from O(n) to O(n/p) where p is the number of processors. This dimensional change enables the system to handle large datasets in real-time, providing immediate responses for customer support and security alerting.
Data Source
AI summary
In some examples, iterative sampling based dataset clustering may include sampling a dataset that includes a plurality of items to identify a specified number of sampled items. The sampled items may be clustered to generate a plurality of clusters. Un-sampled items may be assigned from the plurality of items to the clusters. Remaining un-sampled items that are not assigned to the clusters may be identified. A ratio associated with the remaining un-sampled items and the plurality of items may be compared to a specified threshold. Based on a determination that the ratio is greater than the specified threshold, an indication of completion of clustering of the plurality of items may be generated.