Mixture Model Construction via Dataset Segmentation
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Solution Overview
Problem
Current data mining methods lack efficiency in generating accurate models for complex datasets, particularly in exploring predictive patterns, anomaly detection, and data segmentation across diverse applications.
Innovation Solution
A method is introduced to generate a general mixture model by partitioning a dataset into subsets, creating subset mixture models, and combining them, with optional filtering and simplification to enhance model accuracy and flexibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional mixture models are used to model complex datasets, then model generality is maintained, but model accuracy and efficiency deteriorate
Solution Approach 1:
The patent divides the complex dataset into multiple subsets and creates separate mixture models for each subset. This segmentation allows each subset model to be more accurately fitted to its specific data characteristics, improving overall model accuracy while maintaining computational efficiency through parallel processing of subsets.
2Adaptability or versatility
If mixture models are constructed by identifying clusters and fitting mathematical functions, then model representational capability is improved, but computational complexity increases
Solution Approach 1:
By segmenting the dataset into subsets and fitting mixture models to each subset separately, the patent reduces the computational complexity of fitting a single large-scale mixture model while maintaining or improving representational capability through specialized subset models.
Solution Approach 2:
The patent combines multiple subset mixture models into a general mixture model that can represent the entire dataset. This merging approach maintains high representational capability by integrating insights from multiple subsets while managing computational complexity through modular model construction.
3Adaptability or versatility
If a single general mixture model is used for the entire dataset, then model simplicity is maintained, but ability to capture diverse patterns deteriorates
Solution Approach 1:
The patent segments the dataset into multiple subsets and creates specialized mixture models for each subset, enabling the system to capture diverse patterns and characteristics that would be missed by a single general model. This segmentation enhances pattern detection capability while managing model complexity through modular architecture.
Data Source
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AI summary
A method of constructing a general mixture model (100) of a dataset includes partitioning the dataset into at least two subsets (104) according to predefined criteria (108), generating a subset mixture model for each of the at least two subsets (110), and then combining the mixture models from each subset to generate a general mixture model (120).