Dataset Splitting via Histogram Elbow Point Detection
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Solution Overview
Problem
Existing statistical analysis techniques for dividing empirical distributions are either computationally expensive or stochastic in nature, often failing to obtain an optimal split point due to their random nature or convergence issues.
Innovation Solution
A computer-implemented method that generates a histogram distribution of a dataset, identifies an elbow/knee point by analyzing y-axis values, determines histogram bin significance using a central tendency value, and splits the dataset into a head and tail dataset at the most extreme difference histogram bin.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing statistical analysis techniques are used to divide empirical distributions, then the analysis can be performed, but the computational cost is high and the results are stochastic rather than optimal
Solution Approach 1:
The patent segments the dataset into discrete histogram bins with defined boundaries, transforming continuous data into discrete segments. This segmentation enables deterministic identification of split points by analyzing bin boundaries and significance values, avoiding stochastic methods while maintaining computational efficiency through structured data organization
Solution Approach 2:
The patent changes the parameter representation by using histogram bin significance values and elbow/knee point coordinates as deterministic parameters for identifying optimal split points. Instead of relying on stochastic convergence, the method uses calculated parameters (bin significance, elbow point coordinates) to precisely determine split locations, improving measurement precision while controlling computational cost
2Measurement precision
If stochastic methods are used to find split points, then the analysis can proceed, but optimal split points are not obtained due to random nature
Solution Approach 1:
The patent implements feedback through iterative analysis of histogram distributions to identify elbow/knee points. The method repeatedly analyzes the histogram, calculates bin significance values, and refines the identification of extreme difference bins until the optimal split point is determined. This feedback loop ensures deterministic convergence to optimal split points without relying on random initialization or stochastic convergence
Solution Approach 2:
The patent performs preliminary actions by first generating the complete histogram distribution and calculating all bin significance values before identifying the optimal split point. By pre-processing the data into a structured histogram with calculated significance metrics, the method eliminates the need for stochastic search methods, ensuring both optimality and reliability in split point identification
Data Source
AI summary
A computer-implemented method includes receiving a dataset. The method further includes generating a histogram distribution of the dataset. The method further includes identifying an elbow/knee point by iteratively analyzing the histogram distribution based on y-axis values. The method further includes determining histogram bin significance of the histogram distribution using a central tendency value. The method further includes determining a most extreme difference histogram bin of the histogram distribution based on the histogram distribution, the elbow/knee point, and the histogram bin significance. The method further includes mapping the most extreme difference histogram bin to the dataset. The method further includes splitting the dataset into a head dataset and a tail dataset at the most extreme difference histogram bin. The method further includes outputting an indication of the head dataset and the tail dataset.