Iterative Vector Clustering for Faster High-Dimensional Convergence
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Solution Overview
Problem
Existing clustering algorithms are sensitive to initial placement of cluster centers and suffer from the curse of dimensionality, making them unsuitable for time-constrained scenarios such as cybersecurity threat analysis and telecommunications network reconfiguration.
Innovation Solution
An iterative clustering method that reassesses the placement of vectors by reassigning outermost vectors to the nearest neighboring clusters, using fitness metrics like silhouette coefficient and between-cluster distance to refine the clustering solution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If gradient descent is used to find maxima or minima in clustering algorithms, then the algorithms can optimize cluster placement, but they become highly sensitive to initial placement of cluster centers and slow to converge
Solution Approach 1:
The patent applies preliminary action by using a space-partitioning data structure (such as a k-d tree or ball tree) to pre-organize the data points before clustering begins. This preprocessing step creates a hierarchical structure that enables efficient nearest-neighbor searches during the clustering process, eliminating the need for slow gradient descent iterations while maintaining high clustering accuracy through intelligent initial cluster center placement.
2Adaptability or versatility
If traditional clustering algorithms are used, then they can group data into categories, but they suffer from the curse of dimensionality and require significant computational resources
Solution Approach 1:
The patent introduces an intermediary space-partitioning data structure that acts as a mediator between the high-dimensional data points and the clustering algorithm. This intermediate structure organizes data in a way that reduces the computational complexity of distance calculations and cluster assignments, making the clustering process scalable to high-dimensional datasets without sacrificing clustering capability.
3Measurement precision
If manual labeling is used to train machine learning models, then the models can be trained accurately, but it is time-consuming and resource-intensive
Solution Approach 1:
The patent applies self-service by implementing unsupervised clustering that automatically groups unlabeled data points into meaningful categories without requiring manual annotation. The space-partitioning approach enables the algorithm to self-organize the data structure and identify clusters independently, eliminating the time-consuming manual labeling process while still producing accurate cluster assignments that can be used for model training.
Data Source
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AI summary
A computer-implemented method of iteratively clustering a plurality of vectors, the computer-implemented method comprising generating a candidate solution based on a current solution by reassigning an outermost vector of a selected cluster to a cluster of a plurality of vector clusters containing a vector of the plurality of vector clusters other than the selected cluster with the shortest distance from that outermost vector.