Restrictive Clustering via Boundary Conditions

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Solution Overview

Problem

Conventional clustering algorithms for large datasets are computationally intensive and require numerous iterations, leading to high energy consumption and human intervention, which is inefficient and error-prone.

Innovation Solution

A system that superimposes boundary conditions onto clustering algorithms to reduce iterations by determining cluster and segment thresholds, using a database and server arrangement with modules for data extraction, mapping, clustering, regression, and restrictive clustering, allowing for supervised clustering without human intervention.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If conventional clustering algorithms (k-means, hierarchical clustering) are used for large datasets, then clustering operation can be performed, but the process becomes computationally intensive and requires numerous iterations

Engineering Contradiction:

Improveclustering operation speedVSAvoidcomputational energy consumption

Core Design Contradiction:

ProductivityVSUse of energy by moving object

Solution Approach 1:

The patent applies preliminary action by performing regression analysis and determining boundary conditions before executing the clustering algorithm. The system calculates segment thresholds and establishes boundary conditions that constrain the clustering process, thereby reducing the number of iterations needed and lowering computational energy consumption while maintaining clustering effectiveness

Inventive Principle:

Principle #10Preliminary action

2Measurement precision

If unsupervised clustering algorithms perform numerous iterations to achieve optimal output, then clustering accuracy is improved, but the process becomes time-intensive and compute-intensive

Engineering Contradiction:

Improveclustering accuracyVSAvoidclustering processing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system performs preliminary regression analysis to determine segment thresholds and establish boundary conditions before clustering. This pre-processing step provides guidance for the clustering algorithm, reducing the number of iterations required to achieve accurate results and thereby decreasing processing time while maintaining clustering precision

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent changes the parameter space by introducing boundary conditions and segment thresholds derived from regression analysis. These parameters constrain the clustering algorithm's search space, enabling faster convergence to accurate results without requiring numerous iterations, thus reducing processing time while maintaining precision

Inventive Principle:

Principle #35Parameter changes

3Reliability

If human intervention is used to determine clustering parameters for small datasets, then clustering quality is improved, but the process requires significant human involvement

Engineering Contradiction:

Improveclustering qualityVSAvoidautomation level

Core Design Contradiction:

ReliabilityVSExtent of automation

Solution Approach 1:

The system applies self-service by automatically performing regression analysis, determining segment thresholds, and establishing boundary conditions without human intervention. The algorithm autonomously prepares the constrained clustering framework and executes the clustering process, maintaining high reliability while achieving full automation suitable for large datasets

Inventive Principle:

Principle #25Self-service

4Use of energy by moving object

If boundary conditions are superimposed onto clustering algorithms, then iterations are reduced and computational intensity decreases, but the system complexity increases

Engineering Contradiction:

Improvecomputational energy consumptionVSAvoidsystem structural complexity

Core Design Contradiction:

Use of energy by moving objectVSDevice complexity

Solution Approach 1:

The patent applies segmentation by dividing the clustering process into distinct phases: regression analysis phase for determining boundary conditions, and constrained clustering phase for executing clustering with pre-established constraints. This segmentation organizes the complexity into manageable modules, reducing computational energy consumption while maintaining acceptable system structural complexity through clear functional separation

Inventive Principle:

Principle #1Segmentation

Data Source

PatentUS11423059B2System and method for restrictive clustering of datapoints

Publication Date: 2022.08.23 INNOPLEXUS AG

AI summary

Disclosed is a system for restrictive clustering of datapoints. The system comprises server arrangement that acquires data record for performing clustering operation, determines datapoints for the data record, plots the datapoints in a multi-dimensional space, determines a cluster threshold, and performs a first iteration of clustering on the datapoints plotted in the multi-dimensional space, determines a segment threshold for the datapoints plotted in the multi-dimensional space, derives boundary conditions for determining segments based on the segment threshold and superimposes the boundary conditions corresponding to each of the segments based on the segment threshold onto the first iteration of clustering. Moreover, the server arrangement re-iterates the first iteration of clustering to obtain a second iteration of clustering, wherein the second iteration of clustering has an error value lower than an error value for the first iteration of clustering.