Non-Adjacent Data Subset Comparison via Hierarchical Segmentation
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Solution Overview
Problem
Current data analysis techniques are limited in comparing and visualizing non-adjacent data subsets, as they primarily focus on adjacent subsets and lack the capability to identify statistically significant patterns across non-adjacent subgroups.
Innovation Solution
A system and process that utilizes unsupervised, semi-supervised, and supervised machine learning techniques to identify statistically significant patterns in non-adjacent data subsets, allowing for the analysis and visualization of these patterns through a user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If current business intelligence tools display adjacent data subsets using charts, then data visualization is achieved, but the capability to compare non-adjacent data subsets is lost
Solution Approach 1:
The patent segments data subsets by defining adjacency relationships based on hierarchical paths. Non-adjacent subsets are identified as those with different parent nodes or different positions in the hierarchy, allowing selective comparison of specific data segments while maintaining visualization capabilities.
Solution Approach 2:
The patent introduces a new dimension for data comparison by enabling direct comparison of non-adjacent subsets that differ in hierarchical position. This extends traditional adjacent-comparison (one-dimensional) to multi-dimensional comparison across different hierarchy levels and branches.
2Shape
If decision trees show hierarchical splits in data, then hierarchical relationships are visualized, but non-hierarchical comparison of non-adjacent subsets is restricted
Solution Approach 1:
The patent makes the comparison system dynamic by allowing users to select and compare any two data subsets regardless of their hierarchical relationship. The system adapts to display both hierarchical paths and direct comparisons, transitioning between different comparison modes as needed.
Solution Approach 2:
The patent creates a universal comparison framework that handles both adjacent and non-adjacent subsets, hierarchical and non-hierarchical relationships. The same interface and methods are used for all comparison types, making the system multi-functional rather than requiring separate tools.
3Measurement precision
If data subsets are defined by single coordinate differences, then adjacent subsets are easily identified, but non-adjacent subsets cannot be compared
Solution Approach 1:
The patent changes the parameter for subset identification from single-coordinate difference to multi-coordinate comparison. Non-adjacent subsets are identified by having different values in one or more coordinates, allowing the system to recognize and compare subsets that differ in multiple dimensions simultaneously.
Data Source
AI summary
Systems, methods, computing platforms, and storage media for comparing non-adjacent data subsets are disclosed. Exemplary implementations may: receive an input data set, the input data set including information to be analyzed; generate at least one list of data subsets of the input data set; determine whether at least one data subset of the at least one list of data subsets contains a notable characteristic; identify at least one data pattern in the at least one list of data subsets of the input data set; sort data subsets of the at least one list of data subsets of the input data set; and display, via a user interface, the data subsets of the at least one list of data subsets, based on the sorting.