Visualization Template for Data Cluster Comparison
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Solution Overview
Problem
Existing clustering algorithms for process data visualization in fields like medical treatment require deep understanding and customization, making it challenging to visually analyze and compare clustering results effectively within the context of the process.
Innovation Solution
A method using a hardware processor to construct a visualization template with graphical attributes for each stage of a process, allowing for the visualization of differences between data clusters by generating instances of the template that represent multiple data clusters, with attributes such as color, size, and shape representing parameter values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing clustering algorithms are used for process data, then data can be processed and clustered, but the visualization requires deep understanding of the process and extensive customization for each process type
Solution Approach 1:
The patent creates a universal visualization template that can represent multiple process types (medical treatment, manufacturing, service processes) through a standardized node-connector structure. Each node represents a process stage with configurable parameters, allowing the same template framework to adapt to different domains without requiring complete customization for each process type
Solution Approach 2:
The visualization template uses configurable parameters at each node (such as treatment parameters for medical processes or process variables for manufacturing) that can be adjusted to match different process types. This allows the underlying visualization structure to remain consistent while the specific parameter values and meanings change according to the process being analyzed
2Loss of information
If clustering results are visualized without a standardized template, then flexibility is maintained, but meaningful visual analysis and comparison of clustering results becomes difficult
Solution Approach 1:
The patent segments the visualization into standardized components: nodes representing process stages, connectors representing transitions, and parameter attributes associated with each node. This segmentation allows systematic comparison of clustering results by breaking down complex processes into comparable units while preserving the interpretive context of each segment
Solution Approach 2:
The standardized template acts as a reusable copy that can be instantiated multiple times to visualize different clustering results. By copying the same structural template and populating it with cluster-specific data, the patent enables consistent visual comparison across different clustering outcomes without losing process-specific interpretation information
Data Source
AI summary
A method comprising using at least one hardware processor for: receiving multiple data clusters, each comprising one or more path variations of a process performed with respect to multiple subjects, wherein each of the path variations comprises multiple stages of the process, and wherein at least some of the stages, each comprises one or more parameters; constructing a visualization template representative of the path variations, wherein the visualization template comprises multiple nodes, each node having one or more graphical attributes, wherein each node representative of a corresponding stage; assigning each of the graphical attributes of each of the nodes to a corresponding parameter of the corresponding stage; and visualizing one or more differences between the data clusters by generating at least one instance of the visualization template, the instance being representative of and corresponding to at least two of the data clusters, wherein each of the at least one instance is representative of and corresponding to at least one of the data clusters, and wherein in the at least one instance, each of the assigned one or more graphical attributes of each node represent a value of the corresponding parameter, the value relating to the corresponding stage of the at least one corresponding data cluster.