Systems and methods for data analysis

The method computes tessellation cells and identifies clustered data points based on density thresholds to analyze large data sets efficiently, reducing computational overhead and eliminating bias, thus enhancing data analysis.

WO2025207615A1 Publication Date: 2025-10-02OHIO STATE INNOVATION FOUND +1
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Patent Information

Application Number
PCT/US2025/021316
Authority / Receiving Office
WO · WO
Patent Type
Applications
Current Assignee / Owner
Priority Date
2024-03-25
Filing Date
2025-03-25
Publication Date
2025-10-02

AI Technical Summary

Technical Problem

Existing methods for data analysis are computationally inefficient, limiting the insights that can be gleaned from large, unstructured data sets, and often require hyperparameters that introduce bias.

Method used

The method involves computing a tessellation cell for each input data point, removing unbounded cells, calculating instantaneous spatial density, identifying clustered data points based on a density threshold, and grouping them into clusters, followed by analyzing these clusters in continuous or discrete space to identify relationships, without the need for hyperparameters.

Benefits of technology

This approach reduces computational overhead and runtime, allowing for efficient analysis of complex data sets at all spatial scales with improved accuracy and eliminates bias by avoiding the use of hyperparameters.

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Abstract

Provided herein are methods for analysis of a data set comprising a plurality of input data points in the form of numbers or multidimensional coordinates. These methods can comprise: (a) processing the plurality of input data points to define a study area; (b) computing a tessellation cell for each of the plurality of input data points; (c) removing any tessellation cells that are unbounded or lie outside of the study area; (d) computing an instantaneous spatial density at each of the plurality of input data points using the tessellation cell; (e) identifying clustered data points as those whose instantaneous spatial density exceeds an analytically defined density threshold; and (f) grouping clustered data points into ensemble point clusters (clustered points with adjacent tessellation cells.
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