Subject Process Modification Using Harmonized Outlier Clusters
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Solution Overview
Problem
Existing data analysis systems fail to accurately represent complex phenomena due to inadequate user-provided data intake and processing capabilities, leading to inaccuracies in iterative analysis.
Innovation Solution
An apparatus and method for determining an instruction set that includes a processor and memory to receive datasets, generate outlier clusters, classify them using a trained classifier, and generate an interface data structure for remote display, enabling improved data representation and analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If prior programmatic attempts are used to resolve data accuracy issues, then some level of data processing is achieved, but the accuracy and reliability of representing complex phenomena remain insufficient
Solution Approach 1:
The system segments the analysis by generating multiple outlier clusters from different datasets, where each cluster represents a distinct pattern or phenomenon. This segmentation allows for more precise measurement of individual patterns while maintaining overall reliability through the collective representation of multiple clusters.
Solution Approach 2:
The system implements feedback by using trained classifiers to evaluate and refine the outlier clusters. The classifiers provide feedback on the accuracy of cluster representations, enabling iterative improvement of both measurement precision and representation reliability through continuous evaluation and adjustment.
2Loss of information
If multiple datasets are processed to generate multiple outlier clusters, then comprehensive data analysis is achieved, but the complexity of data processing increases
Solution Approach 1:
The system merges multiple outlier clusters into a unified framework where they can be collectively analyzed and compared. This merging approach maintains the completeness of information from multiple datasets while reducing processing complexity by treating the clusters as integrated units rather than separate analysis tasks.
Solution Approach 2:
The trained classifier serves as a universal tool that can evaluate multiple outlier clusters across different datasets. This multi-functional approach allows the same processing mechanism to handle diverse cluster types, reducing overall system complexity while maintaining comprehensive data analysis capabilities.
3Measurement precision
If a trained classifier is used to classify harmonized outlier clusters, then classification accuracy is improved, but the time and computational resources required increase
Solution Approach 1:
The system performs preliminary action by training the classifier in advance on representative data before actual classification tasks. This pre-training establishes a ready-to-use model that can quickly and accurately classify harmonized outlier clusters, reducing the time required during actual operation while maintaining high classification accuracy.
4Reliability
If harmonized outlier clusters are generated from multiple clusters, then the representation of complex phenomena is enhanced, but the computational effort required increases
Solution Approach 1:
The system extracts key characteristics and patterns from multiple individual outlier clusters to create the harmonized cluster. By taking out only the essential features needed for accurate phenomena representation rather than processing complete raw data, the system enhances representation reliability while reducing computational energy requirements.
Data Source
AI summary
An apparatus and method for determining an instruction set is provided. The apparatus includes a processor and a memory connected to the processor. The memory contains instructions configuring the processor to receive multiple datasets, where each dataset describes actions performed by an entity and to generate, for each dataset of the datasets, an outlier cluster. Generating the outlier cluster includes aggregating data included in a dataset, identifying data within aggregated data based on similarity to actions, and assigning a quality score for the identified data based on assessing whether identified data exceeds a threshold value. The outlier cluster is determined based on the quality score. The processor may receive a subject process describing a current state of the entity and identify, for each outlier cluster, a process modification model that describes a set of actions to be performed to increase proximity of the subject process to each outlier cluster.