Density-Based Clustering via Lattice Point Segmentation
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Solution Overview
Problem
Existing methods struggle to efficiently analyze large populations with multiple measurable properties, such as cells or market participants, due to the complexity and volume of data, particularly in identifying and clustering sub-populations effectively.
Innovation Solution
A computer-implemented method using lattice points, weights, and density functions to identify clusters by creating directional associations and assigning data items to lattice points, allowing for automated clustering and data reduction, even in high-dimensional spaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional clustering methods are used to analyze large populations with multiple measurable properties, then comprehensive data analysis is achieved, but computational complexity and processing time increase significantly
Solution Approach 1:
The patent segments the large population data into multiple clusters by identifying sub-populations with distinct characteristics. The lattice-based approach divides the data space into discrete regions, allowing efficient processing of large datasets by handling segmented portions rather than the entire dataset as a single complex unit.
Solution Approach 2:
The patent introduces lattice points as intermediary structures between raw data and cluster representations. These lattice points serve as mediators that simplify the computational process by providing a structured framework for density calculation and cluster identification, reducing the direct computational burden on the original large-scale data.
2Measurement precision
If detailed analysis of all data points is performed, then clustering accuracy is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary actions by pre-defining the lattice structure and calculating lattice point positions before processing the actual clustering. This preliminary setup creates a ready-made framework that accelerates subsequent cluster identification operations, avoiding the need to process all data points from scratch during the main analysis phase.
Solution Approach 2:
The patent replaces traditional mechanical data processing approaches with a lattice-based computational model. Instead of directly analyzing all data points through computationally intensive algorithms, the system substitutes this with lattice point density calculations, which are mathematically equivalent but computationally more efficient for large datasets.
3Adaptability or versatility
If manual clustering methods are used, then flexibility in analysis is maintained, but automation and efficiency are reduced
Solution Approach 1:
The patent creates a universal lattice-based clustering framework that can handle various types of data and clustering scenarios through a single unified approach. The lattice structure and density calculation methods are adaptable to different data dimensions and distributions, providing both automation and flexibility without requiring separate manual procedures for different cases.
Solution Approach 2:
The patent implements self-service clustering where the lattice structure automatically adapts to the data distribution through density calculations. The system performs cluster identification autonomously by following directional associations between lattice points, eliminating the need for manual intervention while maintaining adaptability to different data characteristics.
4Measurement precision
If high-dimensional data is analyzed in detail, then measurement precision is improved, but computational resources required increase
Solution Approach 1:
The patent transforms the high-dimensional data analysis problem into a lattice-based framework where directional associations between lattice points capture the essential multi-dimensional relationships. This dimensionality transformation allows the system to analyze high-dimensional data more efficiently by projecting complex relationships onto the lattice structure, reducing computational resource requirements while maintaining measurement precision.
Data Source
AI summary
The described invention provides a method and/or system for analyzing data using population clustering through density based merging, and a method for guiding clustering strategy through entropy-based ranking score.