Histogram Bin Interval Approximation for Clear Labeling
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Solution Overview
Problem
Existing histogram visualization techniques struggle with clear labeling of bin intervals, especially when dealing with large values or uneven ranges, leading to data visualization noise and diminished clarity.
Innovation Solution
A computer system and method that determines approximated bin intervals for histograms by applying a bin mask based on the bin width to the feature statistics, ensuring clear and intuitive labeling of bin intervals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If bin width is set to accommodate large values or uneven ranges, then the histogram can cover the full data range, but the bin interval labels become unclear and difficult to interpret
Solution Approach 1:
The patent transforms the bin width parameter from a fixed numerical value to a scaled representation. By introducing a scaling factor that relates bin width to a base unit (e.g., bin width = k × base_unit), the system can adapt to large values and uneven ranges while keeping the displayed labels in terms of the base unit, thus maintaining clarity.
Solution Approach 2:
The patent introduces a scaling factor as an intermediary between the actual bin width and the displayed bin labels. This mediator allows the system to work with large or uneven bin widths internally while presenting simplified, human-readable labels to users, resolving the contradiction between adaptability and clarity.
2Measurement precision
If bin width is reduced to provide more detailed intervals, then the histogram shows finer granularity, but the number of bins increases leading to visualization noise
Solution Approach 1:
The patent changes the parameter representation by introducing a scaling factor that allows bin widths to be expressed as multiples of a base unit. This enables the system to maintain fewer, larger bins (reducing noise) while still providing detailed granularity through the scaled labeling system, where labels show the relationship to the base unit rather than raw numerical values.
3Loss of information
If bin intervals are labeled with exact numerical values, then precise interval information is provided, but the labels become lengthy and incomprehensible for large values
Solution Approach 1:
The patent transforms the labeling parameter from displaying exact numerical bin boundaries to displaying scaled relationships. Instead of showing raw values like '1234-2345', the system shows labels based on the scaling factor (e.g., '1-2 base units'), preserving the interval information while making it comprehensible through the scaled representation.
Solution Approach 2:
The scaling factor serves as an intermediary that translates between the actual bin interval values and the displayed labels. This mediator preserves the precise interval information internally while presenting simplified, comprehensible labels to users, resolving the contradiction between information precision and label comprehensibility.
Data Source
AI summary
Using approximated bin intervals to label the histograms provides clarity and allows for the histogram to be more intuitively understood. A dataset may comprise a plurality of records having a plurality of features including one or more continuous features. A selection of a continuous feature may be obtained. A bin width based on a number of bins and feature statistics of the continuous feature may be determined. An approximated bin interval range is determined by applying a bin mask based on the bin width to the feature statistics. An approximated bin width is determined based on the number of bins and the approximated bin interval range. Approximated bin intervals for the histogram are determined based on the approximated bin width. A histogram is generated having bins with intervals based the approximated bin intervals.