Clustering Meta-Learning Model for Automated Pipeline Selection
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Solution Overview
Problem
Current clustering methods struggle to effectively group data into similar groups relative to other data points within a dataset, as they often rely on unsupervised approaches that lack specificity and fail to match the right clustering algorithm with the specific data set, leading to inefficient clustering results.
Innovation Solution
A machine learning clustering meta-learning model is developed that converts unsupervised clustering problems into supervised learning by creating a repository of labeled datasets, using internal and external scores to generate a supervised model that predicts the best clustering pipelines for unseen datasets, employing techniques like imputation, scaling, and feature engineering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If unsupervised clustering approaches are used, then clustering can be performed without labeled data, but the ability to match specific clustering algorithms with specific datasets is poor
Solution Approach 1:
The system performs preliminary actions by creating a repository of labeled clustering datasets in advance, training supervised learning models to learn the mapping between dataset characteristics and optimal clustering algorithms. This preliminary training enables the system to quickly match algorithms to new datasets without losing the benefit of labeled data insights.
Solution Approach 2:
The patent introduces an intermediary supervised learning model that mediates between the dataset characteristics and clustering algorithm selection. This intermediary model learns from labeled data to predict which clustering algorithm will work best for a given dataset, bridging the gap between unsupervised clustering needs and supervised learning capabilities.
2Measurement precision
If multiple clustering algorithms are evaluated to find the best match, then clustering accuracy improves, but computational time and complexity increase
Solution Approach 1:
The system performs preliminary evaluation of multiple clustering algorithms during the training phase on labeled datasets. By pre-learning the performance characteristics of different algorithms across various data types, the system can quickly recommend the best algorithm for new datasets without performing exhaustive evaluations at runtime.
Solution Approach 2:
The patent creates a copy of the knowledge gained from evaluating multiple algorithms on training data by storing this information in a supervised learning model. Instead of re-evaluating algorithms on each new dataset, the system copies the learned patterns and recommendations, significantly reducing the time required for algorithm selection while maintaining accuracy.
3Measurement precision
If supervised learning is used to select clustering algorithms, then algorithm matching precision improves, but the requirement for labeled data increases complexity
Solution Approach 1:
The patent creates a universal supervised learning model that serves multiple functions: it learns from labeled clustering datasets, predicts optimal clustering algorithms for new datasets, and provides recommendations without requiring complex manual configuration. This multi-functional approach simplifies the overall system by consolidating algorithm selection expertise into a single model.
Solution Approach 2:
The supervised learning model enables the system to self-service by automatically selecting appropriate clustering algorithms based on dataset characteristics. The model learns from existing labeled data and autonomously makes algorithm recommendations, reducing the need for manual expert intervention and simplifying the user interface.
Data Source
AI summary
A method of creating a machine learning clustering meta learning model for use in solving a machine learning clustering problem includes obtaining a plurality of information related to the machine learning clustering problem, wherein the plurality of information includes classification datasets, machine learning transformers and clustering estimators, creating a set of clustering datasets using the classification datasets, generating trained clustering pipelines by training the set of unsupervised clustering pipelines responsive to the clustering datasets, processing the trained clustering pipelines to generate internal scores and external scores for the set of clustering datasets, creating an encoded clustering pipeline by encoding the trained clustering pipeline using the external score as a label and generating a trained supervised machine learning model by combining the internal scores and the encoded clustering pipelines.