Automated K-Means Clustering with Dynamic Index Selection
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Solution Overview
Problem
Conventional k-means clustering methods face challenges in determining the optimal k value, leading to inefficient and unstable clustering results, especially in high-dimensional data and distributed processing environments, where the choice of k significantly affects convergence time and result accuracy.
Innovation Solution
A cluster analysis supporting system and method that automates the k-means clustering process by performing k-means clustering within a predetermined range of k values and preset iteration frequencies, selecting an optimum center value, and updating the k value based on index calculations, ensuring stable and efficient clustering across a distributed environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If k-means clustering is performed with a randomly determined k value, then the clustering process can be executed, but the clustering result accuracy deteriorates and requires skilled operator control
Solution Approach 1:
The patent changes the parameter k from a fixed random value to a dynamically determined value through index calculation. The system calculates an optimal k value by analyzing data characteristics and performing index calculations, transforming the clustering process from one requiring manual skill to an automated systematic approach that adapts parameters based on data properties.
Solution Approach 2:
The clustering system performs self-service by automatically determining the optimal k value without requiring skilled operator intervention. The system calculates indices based on data characteristics and autonomously selects the appropriate k value, making the clustering process self-sufficient and eliminating dependency on operator expertise.
2Reliability
If k-means clustering is performed with a great number of data pieces or high dimension, then comprehensive data coverage is achieved, but the execution time increases significantly
Solution Approach 1:
The patent applies preliminary action by calculating the optimal k value before performing the full k-means clustering process. By determining the appropriate number of clusters in advance through index calculation on representative data, the system avoids unnecessary iterative computations with suboptimal k values, thereby reducing overall execution time while maintaining clustering reliability on large-scale data.
3Measurement precision
If the center value is changed to improve clustering results, then clustering accuracy may be improved, but the convergence time and result consistency deteriorate
Solution Approach 1:
The patent implements feedback by calculating indices based on clustering results and using this information to determine the optimal k value. The system performs index calculations that provide feedback on clustering quality, allowing it to adjust and select the most appropriate k value while maintaining result consistency across different center value initializations.
4Productivity
If skilled operator control is used to manage clustering efficiency, then some level of optimization is achieved, but the system complexity and operational difficulty increase
Solution Approach 1:
The patent eliminates the need for skilled operator control by implementing self-service through automated index calculation. The system independently determines the optimal k value by calculating indices based on data characteristics, replacing complex manual control procedures with an automated algorithmic approach that maintains efficiency without requiring operator expertise or increasing system complexity.
Data Source
AI summary
Disclosed is a cluster analysis supporting system, with respect to providing a cluster analysis function, including a cluster analysis service apparatus configured to request a distributed processing service apparatus to perform a k-means clustering based on k values within a predetermined range and a preset iteration frequency until a predefined converge condition is satisfied, and if center values of the k values are calculated from the distributed processing service apparatus, select an optimum center value among the center values, and control calculation and application of an optimum k value through an index calculation with respect to applying clustered indexes assigned based on the selected optimum center value to data, and the distributed processing service apparatus configured to perform the k-means clustering based on the k values and the preset iteration frequency provided from the cluster analysis service apparatus upon the request by the cluster analysis service apparatus.