Descriptive Vector Clustering for Balanced Hierarchy Selection
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Solution Overview
Problem
Existing data clustering methods often result in unbalanced clusters, either having a few excessively large or many excessively small groups, leading to inefficient organization and categorization of data items.
Innovation Solution
A clustering machine that calculates vector distances and generates a tiered hierarchy, determining centroid vectors and scores based on intra- and inter-cluster distances, selecting a subset of the hierarchy to achieve balanced clusters through a scalar-based optimization process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data items are clustered using conventional methods, then clustering is achieved, but the clusters become unbalanced with a few excessively large or many excessively small groups
Solution Approach 1:
The patent applies parameter changes by adjusting the scalar value in the scoring function to control the balance of cluster sizes. By varying the scalar parameter, the system can shift the clustering outcome from unbalanced (few large clusters) to balanced (moderate-sized clusters), directly addressing the cluster balance issue while maintaining clustering efficiency
Solution Approach 2:
The patent introduces a dynamic scoring mechanism that evaluates clusters based on both intra-cluster and inter-cluster distances with a adjustable scalar. This dynamic evaluation allows the system to adaptively select optimal cluster configurations, transforming static clustering results into dynamically optimized balanced clusters
2Ease of operation
If conventional clustering methods are used, then data items are grouped, but the organization and categorization becomes inefficient
Solution Approach 1:
The patent implements a feedback mechanism through the scoring function that continuously evaluates cluster quality based on intra-cluster and inter-cluster distances. This feedback loop allows the system to automatically adjust and select optimal cluster configurations, improving organization efficiency without requiring complex manual intervention
Solution Approach 2:
The clustering system performs self-optimization by automatically evaluating and selecting balanced cluster configurations through the scoring mechanism. The system serves itself by adjusting cluster balances and selecting optimal scalars without external intervention, simplifying the overall process while maintaining high organization efficiency
Data Source
AI summary
A clustering machine can cluster descriptive vectors in a balanced manner. The clustering machine calculates distances between pairs of descriptive vectors and generates clusters of vectors arranged in a hierarchy. The clustering machine determines centroid vectors of the clusters, such that each cluster is represented by its corresponding centroid vector. The clustering machine calculates a sum of inter-cluster vector distances between pairs of centroid vectors, as well as a sum of intra-cluster vector distances between pairs of vectors in the clusters. The clustering machine calculates multiple scores of the hierarchy by varying a scalar and calculating a separate score for each scalar. The calculation of each score is based on the two sums previously calculated for the hierarchy. The clustering machine may select or otherwise identify a balanced subset of the hierarchy by finding an extremum in the calculated scores.