Clustered Data Visualizer Using Heat Map Segmentation
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Solution Overview
Problem
Visualizing clustered datasets is challenging due to the inability to present them in a comprehensible manner, as clustering algorithms divide data without providing intelligible reasoning, leading to cluttered and confusing visualizations that obscure valuable patterns and insights.
Innovation Solution
A clustered data visualizer that uses multiple colors and color gradients to represent different clusters, generating heat maps and matrices that enable the identification of patterns and correlations, and provides interactive visualizations through a user interface to facilitate data interpretation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If clustering algorithms are used to divide datasets, then data can be organized into groups, but the visualization becomes cluttered and confusing without providing intelligible reasoning
Solution Approach 1:
The patent segments the visualization into multiple heat maps, where each heat map corresponds to a specific cluster from the clustered dataset. This segmentation allows each cluster to be visualized separately with its own color scheme, making patterns within each cluster detectable while maintaining the overall cluster structure. The segmentation resolves the contradiction by organizing divided data into distinct visual segments that are easier to interpret.
Solution Approach 2:
The patent applies local quality by assigning different color schemes to different heat maps corresponding to different clusters. Each heat map uses colors locally appropriate to its cluster, enabling pattern detection within each cluster while maintaining distinction between clusters. This local differentiation resolves the confusion caused by uniform coloring of clustered data.
2Adaptability or versatility
If multiple colors are used to represent different clusters, then cluster differentiation is improved, but visualization complexity increases
Solution Approach 1:
The patent segments the visualization into multiple heat maps, where each heat map corresponds to a specific cluster. This segmentation reduces complexity by allowing each heat map to use a simplified, consistent color scheme rather than requiring a complex multi-color legend system. Each heat map can be independently interpreted, reducing overall visualization complexity while maintaining cluster differentiation.
Solution Approach 2:
The patent uses color changes systematically by assigning different color schemes to different heat maps based on their corresponding clusters. This systematic color assignment provides intuitive cluster differentiation without requiring complex color coding, as users can understand that different colors represent different clusters through the heat map structure itself.
3Loss of information
If detailed cluster information is presented, then data insight is improved, but rendering performance decreases
Solution Approach 1:
The patent segments the detailed cluster information into multiple heat maps, where each heat map presents aggregated statistics for a specific cluster. This segmentation allows detailed information to be presented in an organized manner that is more efficient to render than presenting all details in a single complex visualization. The segmented structure enables progressive rendering and reduces the computational burden of processing and displaying all cluster details simultaneously.
Data Source
AI summary
Various embodiments are generally directed to techniques for visualizing clustered datasets, such as by utilizing multiple colors and multiple color gradients to represent data from different clustered datasets, for instance. Some embodiments are particularly directed to using different colors associated with each cluster of data to visualize which cluster is dominant in each cell of a heat map. Further, in many embodiments, a color gradient may be used among different heat map cells of a common color that correspond to a common cluster to visualize data distributions within each cluster of data represented in the heat map. In multiple embodiments, colors and color gradients may be utilized in conjunction with visualizing clustered datasets to enable identification of useful patterns and relationships among a collection of clustered datasets. In several embodiments, heat maps and/or heat map matrices may be generated and presented via a user interface (UI).