Slime Mold Clustering for Automatic Parameter Adjustment

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Solution Overview

Problem

Conventional clustering methods require setting parameters in advance, which is time-consuming and dependent on individual skills, and do not efficiently achieve high precision clustering results.

Innovation Solution

A clustering apparatus that utilizes slime mold information to cluster data by constructing hub and branch cell information, where target data is associated with branch cell information having the shortest distance, allowing for automatic parameter adjustment and precise clustering without pre-setting parameters.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If conventional clustering methods are used, then clustering can be performed, but parameter setting requires significant effort and individual expertise

Engineering Contradiction:

Improveease of parameter settingVSAvoidtime for parameter setting

Core Design Contradiction:

Ease of operationVSLoss of time

Solution Approach 1:

The slime mold model autonomously determines optimal clustering parameters through its natural growth and adaptation mechanism. The system self-adjusts parameters such as the number of clusters and their positions without requiring external intervention or expert knowledge, allowing the clustering process to serve itself in determining optimal configurations.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The invention dynamically changes clustering parameters based on the slime mold's growth state and environmental feedback. Parameters such as cluster centers, number of clusters, and distribution patterns are automatically adjusted during the simulation process, transforming static parameter setting into dynamic adaptive parameter optimization.

Inventive Principle:

Principle #35Parameter changes

2Measurement precision

If conventional clustering methods are used, then clustering can be performed, but achieving high precision requires inordinate effort in parameter adjustment

Engineering Contradiction:

Improveclustering precisionVSAvoidcomplexity of parameter adjustment

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The slime mold model acts as an intermediary between raw data and clustering results. Instead of directly applying complex algorithms requiring expert tuning, the system introduces a biological simulation layer that naturally processes data relationships and emerges with optimal clustering configurations through its growth and resource allocation behavior.

Inventive Principle:

Principle #24Intermediary (Mediator)

3Productivity

If the number of clusters is fixed in advance, then clustering computation can be performed, but the precision of clustering results is significantly affected

Engineering Contradiction:

Improveclustering computation efficiencyVSAvoidclustering result precision

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The system transitions from static pre-defined cluster numbers to dynamic cluster formation. The slime mold model allows the number of clusters to emerge dynamically based on data distribution and growth conditions, enabling the system to adapt the cluster count during the process rather than fixing it beforehand, thus maintaining both efficiency and precision.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system performs preliminary exploration of the data space through slime mold growth simulation before finalizing cluster configurations. This preliminary action allows the model to naturally discover optimal cluster numbers and positions through its growth pattern, providing a foundation for subsequent precise clustering without requiring advance specification.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS10437794B2Clustering apparatus, clustering method, and storage medium

Publication Date: 2019.10.08 NIPPON CONTROL SYST CORP

AI summary

A clustering apparatus includes: an accepting unit that accepts pieces of target data, each of which is a vector; a slime mold information constructing unit that constructs, using the pieces of target data, slime mold information, which is information having one piece of hub cell information corresponding to a central cell of a slime mold and one or more pieces of branch cell information corresponding to a cell that is divided from the central cell for predation; and a clustering unit that associates each of the pieces of target data with branch cell information having the shortest distance from a vector contained in the branch cell information to the target data, among the one or more pieces of branch cell information contained in the constructed slime mold information, thereby clustering the target data into clusters in the same number as that of the pieces of branch cell information.