Geometric Cluster Detection for High-Dimensional Data Visualization
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Solution Overview
Problem
Existing data visualization tools face challenges in representing and interactively exploring high-dimensional datasets, leading to cluttered visualizations that hinder effective exploration and understanding, especially for non-expert users.
Innovation Solution
A method and system for detecting visual features in datasets by identifying clusters based on geometrical attributes, characterizing these features, and producing visualizations that support interactive exploration, including outlier detection and trend analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If high-dimensional datasets are visualized using traditional methods, then comprehensive data representation is achieved, but visualization clutter increases and explorability deteriorates
Solution Approach 1:
The patent segments high-dimensional data into distinct visual features (clusters, trends, outliers) that can be independently detected and analyzed. This segmentation allows the visualization to maintain completeness while reducing clutter by organizing data into manageable, meaningful groups rather than displaying all data points uniformly.
Solution Approach 2:
The patent transforms high-dimensional data into visual space by detecting geometric attributes and spatial relationships. This dimensionality transformation enables comprehensive data representation in a visual format while maintaining explorability through structured feature detection rather than raw data plotting.
2Measurement precision
If detailed geometric analysis is performed on clusters, then feature characterization accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent performs preliminary geometric analysis by detecting basic cluster properties (centroid, bounding box, shape) before more detailed characterization is needed. This preliminary action establishes accurate geometric foundations that enable precise feature characterization while avoiding unnecessary computational complexity in later processing stages.
Solution Approach 2:
The geometric analysis system characterizes clusters using their own intrinsic geometric attributes (shape, size, orientation) without requiring external reference frameworks. This self-service approach improves characterization accuracy by using data-driven geometric properties while reducing computational complexity by eliminating the need for complex external modeling.
Data Source
AI summary
Embodiments of the invention relate to detecting and describing visible features of a data set. A cluster in a data space is detected. A characteristic associated with the cluster is identified by analysis of the cluster based on geometrical attributes. The analysis includes identification of a shape of the cluster. The identified characteristic is converted into a characterization of the cluster. A visualization is produced based on the characterization.