Hierarchical Clustering With Variable-Importance Explanations
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Solution Overview
Problem
Existing unsupervised machine learning solutions for hierarchical clustering are inefficient and lack interpretability, particularly in large datasets like claims remediation processes, where errors are difficult to detect and resolve due to computational complexity and lack of clarity in cluster analysis.
Innovation Solution
Implementing hierarchical clustering through multiple k-means routines, utilizing intra-cluster variable importance measures, incremental representation measures, and overall variable importance measures to generate interpretable clusters, with predictive explanatory metadata for improved analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional hierarchical clustering methods are used, then clustering can be performed on large datasets, but computational efficiency is poor and interpretability is lacking
Solution Approach 1:
The patent segments the clustering process into multiple k-means routines executed hierarchically, where each routine processes a portion of the data and generates intermediate clusters. This segmentation enables the system to handle large datasets efficiently while maintaining interpretability through the hierarchical structure of the clustering results.
Solution Approach 2:
The patent introduces a hierarchical dimension to the clustering process by organizing clusters into multiple levels. Each level represents a different granularity of clustering, allowing users to navigate from broad groupings to fine-grained clusters. This dimensional approach improves both computational efficiency and interpretability by providing multiple perspectives on the data structure.
2Productivity
If multiple k-means routines are executed hierarchically, then computational efficiency improves, but the system complexity increases
Solution Approach 1:
The complex hierarchical clustering process is segmented into multiple independent k-means routines, each handling a specific level of the hierarchy. This segmentation reduces system complexity by breaking down the overall complex task into simpler, manageable sub-tasks that can be executed independently and efficiently.
Solution Approach 2:
The patent performs preliminary actions by executing coarser-level clustering routines first to establish high-level groupings before proceeding to finer-level routines. This preliminary action simplifies subsequent processing by reducing the data volume and complexity that later routines must handle, thereby improving overall computational efficiency without excessive system complexity.
3Reliability
If detailed cluster analysis is performed, then interpretability improves, but error detection becomes more difficult due to computational complexity
Solution Approach 1:
The hierarchical structure introduces a dimensional framework for error detection, allowing errors to be identified at multiple levels of granularity. Coarser levels provide a high-level overview that makes it easier to detect major errors or anomalies, while finer levels allow for detailed analysis when needed. This multi-dimensional approach maintains interpretability while facilitating error detection.
Solution Approach 2:
The patent performs preliminary error detection at coarser clustering levels before proceeding to detailed analysis at finer levels. This preliminary action filters out obvious errors early in the process, reducing the computational burden of detailed error detection later while maintaining high interpretability through the hierarchical structure.
Data Source
AI summary
There is a need for more effective and efficient hierarchical clustering. This need can be addressed by, for example, solutions for performing interpretable hierarchical clustering. In one example, a method includes performing a group of hierarchical clustering routines to generate a group of final hierarchical clusters; for each cluster-feature pair of a group of cluster-feature pairs, determining an intra-cluster variable importance measure, an incremental representation measure, and an overall variable importance based at least in part on the intra-cluster variable importance measure the incremental representation measure; generating predicted explanatory metadata for the group of hierarchical clustering routines based at least in part on each overall variable importance measure for each cluster-feature pair of the group of cluster-feature pairs; and performing one or more prediction-based actions based at least in part on the predicted explanatory metadata.