Context-Rule Centroids for Automated Component Clustering
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Solution Overview
Problem
Existing clustering algorithms for automated components, such as K-means, randomly select initial cluster centroids, leading to irrelevant or inaccurate cohort groupings and performance analysis in cohort analytics.
Innovation Solution
A K-means clustering algorithm is applied to automated components with an initialized set of cluster centroids selected based on context rules, enhancing logical grouping and performance ranking by leveraging segmentation features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If K-means clustering algorithm randomly selects initial cluster centroids, then the algorithm is simple to implement, but the cohort groupings become irrelevant or inaccurate
Solution Approach 1:
The patent applies preliminary action by initializing cluster centroids using context rules before executing the K-means clustering algorithm. This pre-initialization step uses segmentation features and domain knowledge to establish meaningful starting points for clustering, ensuring that cohorts are formed based on relevant characteristics rather than random assignments, thereby improving grouping accuracy while maintaining algorithmic simplicity
2Measurement precision
If context rules are applied to initialize cluster centroids, then cohort grouping accuracy is improved, but the system complexity increases
Solution Approach 1:
The patent applies parameter changes by modifying the initialization phase of the K-means algorithm to incorporate context rules based on segmentation features. This changes the parameter selection criteria from random to rule-based, improving cohort accuracy. The complexity increase is managed by using predefined context rules rather than complex algorithms, and by leveraging existing segmentation feature extraction capabilities already present in the system
Data Source
AI summary
Systems and methods are described for identifying and resolving performance issues of automated components. The automated components are segmented into groups by applying a K-means clustering algorithm thereto based on segmentation feature values respectively associated therewith, wherein an initial set of centroids for the K-means clustering algorithm is selected by applying a set of context rules to the automated components. Then, for each group, a performance ranking is generated based at least on a set of performance feature values associated with each of the automated components in the group and a feature importance value for each of the performance features. The feature importance values are determined by training a machine learning based classification model to classify automated components into each of the groups, wherein the training is performed based on the respective performance feature values of the automated components and the respective groups to which they were assigned.