Interactive Cluster Visualization for Multidimensional Data Analysis
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Solution Overview
Problem
Existing methods for analyzing large multidimensional datasets are computationally inefficient and fail to identify important relationships, requiring sophisticated experts and non-interactive graphs that hinder exploratory data analysis.
Innovation Solution
A system and method for interactive visualization of data analysis, allowing users to manipulate and reorient visualizations through user actions, with features like node selection, dragging, and color changes based on functions, and regenerating visualizations based on interval values and overlap percentages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional clustering and linear algebraic methods are used to analyze large multidimensional datasets, then computational efficiency is maintained, but important relationships and detailed patterns are lost
Solution Approach 1:
The patent segments the large multidimensional dataset into multiple smaller clusters using a tree-based hierarchical structure. This segmentation allows detailed local analysis within each cluster while maintaining overall computational efficiency through the hierarchical organization, resolving the contradiction between detection precision and computational efficiency.
Solution Approach 2:
The patent transforms the data analysis from traditional linear algebraic methods to a tree-based hierarchical structure, adding a dimensional aspect to the analysis. This dimensional change enables the system to maintain both high detection precision for important relationships and computational efficiency through the structured organization of data clusters.
2Measurement precision
If sophisticated expert analysis is used to interpret data output, then accurate understanding of relationships is achieved, but time consumption and operational complexity increase
Solution Approach 1:
The patent implements self-service through automated generation of visual summaries and key relationship identification from the tree-based cluster analysis. The system automatically highlights important relationships and patterns without requiring sophisticated expert interpretation, thereby maintaining interpretation accuracy while dramatically reducing analysis time and operational complexity.
3Loss of information
If traditional static graphs are used to depict data relationships, then some relationships are visualized, but interactivity and exploratory analysis capabilities are limited
Solution Approach 1:
The patent transforms static relationship graphs into dynamic, interactive visualizations based on the tree-based cluster structure. Users can dynamically explore relationships at different levels of the hierarchy, zoom into specific clusters, and interactively query data patterns, thereby maintaining complete relationship visibility while dramatically improving interactivity and ease of exploratory analysis.
Data Source
AI summary
Exemplary systems and methods for visualization of data analysis are provided. In various embodiments, a method comprises accessing a database, analyzing the database to identify clusters of data, generating an interactive visualization comprising a plurality of nodes and a plurality of edges wherein a first node of the plurality of nodes represents a cluster and an edge of the plurality of edges represents an intersection of nodes of the plurality of nodes, selecting and dragging the first node in response to a user action, and reorienting the interactive visualization in response to the user action of selecting and dragging the first node.