Prioritized Dataset Arrays Through Clustering and Label Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems struggle to efficiently analyze and prioritize large volumes of customer and user data for effective decision-making in dynamic environments, particularly in the context of healthcare data management.
Innovation Solution
A system and method utilizing clustering and classification modules to process datasets, assigning them to appropriate clusters, predicting labels for new datasets, and generating a prioritized array based on derived axes from clustering and classification, enhancing data processing efficiency and user interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional data analysis methods are used to process large volumes of customer and user data, then the system can handle basic data storage, but the analysis efficiency and decision-making capability deteriorate in dynamic environments
Solution Approach 1:
The patent segments the dataset into multiple clusters based on shared characteristics or relationships. Each cluster represents a subset of data with similar properties, enabling parallel processing and faster analysis. The clustering module divides the large dataset into manageable segments that can be independently analyzed and prioritized.
Solution Approach 2:
The patent introduces a new dimension of prioritization by applying classification modules that rank clusters based on their relevance to specific queries or business objectives. This transforms the traditional flat data structure into a multi-dimensional hierarchy where data is organized by clustering characteristics and then prioritized by business value, enabling faster retrieval and analysis.
2Loss of information
If comprehensive data collection is performed to capture all customer and user information, then the data completeness improves, but the system complexity and processing burden increase
Solution Approach 1:
The patent extracts only the most relevant and prioritized data clusters based on query context and business objectives. Instead of processing all collected data, the system identifies and extracts specific clusters that are most relevant to the current analytical task, reducing processing complexity while maintaining data completeness for the required scope.
Solution Approach 2:
The patent applies different processing qualities to different data clusters based on their priority and relevance. High-priority clusters receive more detailed analysis and processing, while lower-priority clusters are processed with less computational resources. This local quality approach optimizes the balance between data completeness and system complexity by allocating processing power where it is most needed.
3Measurement precision
If traditional classification methods are used without clustering, then the classification process is simpler, but the accuracy of predicting labels for new datasets deteriorates
Solution Approach 1:
The patent performs preliminary clustering of the dataset before applying classification. By pre-organizing data into meaningful clusters based on shared characteristics, the system creates a structured foundation that enhances the accuracy of subsequent label predictions. This preliminary action of clustering reduces the search space and improves the effectiveness of the classification module when predicting labels for new datasets.
Data Source
AI summary
A system and method for determining a prioritized array of associated datasets. The system includes at least a processor and a memory communicatively connected to the at least a processor and contains instructions, wherein the at least a processor is configured to receive a plurality of datasets, apply a clustering module to the plurality of datasets, wherein the clustering module is configured to assign an individual dataset to an appropriate cluster, apply a classification module to the plurality of datasets, wherein the classification module is trained on cluster labels of one or more clusters and configured to predict labels for new individual datasets, and generate a prioritized array, wherein generating the prioritized array includes applying the plurality of instances of the plurality of datasets across one or more axes, wherein the one or more axes are derived from the clustering and classification of the plurality of datasets.